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What a Thesis Paper is and How to Write One

From choosing a topic and conducting research to crafting a strong argument, writing a thesis paper can be a rewarding experience.

It can also be a challenging experience. If you've never written a thesis paper before, you may not know where to start. You may not even be sure exactly what a thesis paper is. But don't worry; the right support and resources can help you navigate this writing process.

What is a Thesis Paper?

A thesis paper is a type of academic essay that you might write as a graduation requirement for certain bachelor's, master's or honors programs. Thesis papers present your own original research or analysis on a specific topic related to your field.

“In some ways, a thesis paper can look a lot like a novella,” said Shana Chartier , director of information literacy at Southern New Hampshire University (SNHU). “It’s too short to be a full-length novel, but with the standard size of 40-60 pages (for a bachelor’s) and 60-100 pages (for a master’s), it is a robust exploration of a topic, explaining one’s understanding of a topic based on personal research.”

Chartier has worked in academia for over 13 years and at SNHU for nearly eight. In her role as an instructor and director, Chartier has helped to guide students through the writing process, like editing and providing resources.

Chartier has written and published academic papers such as "Augmented Reality Gamifies the Library: A Ride Through the Technological Frontier" and "Going Beyond the One-Shot: Spiraling Information Literacy Across Four Years." Both of these academic papers required Chartier to have hands-on experience with the subject matter. Like a thesis paper, they also involved hypothesizing and doing original research to come to a conclusion.

“When writing a thesis paper, the importance of staying organized cannot be overstated,” said Chartier. “Mapping out each step of the way, making firm and soft deadlines... and having other pairs of eyes on your work to ensure academic accuracy and clean editing are crucial to writing a successful paper.”

How Do I Choose a Topic For My Thesis Paper?

What your thesis paper is for will determine some of the specific requirements and steps you might take, but the first step is usually the same: Choosing a topic.

“Choosing a topic can be daunting," said Rochelle Attari , a peer tutor at SNHU. "But if (you) stick with a subject (you're) interested in... choosing a topic is much more manageable.”

Similar to a thesis, Attari recently finished the capstone  for her bachelor’s in psychology . Her bachelor’s concentration is in forensics, and her capstone focused on the topic of using a combined therapy model for inmates who experience substance abuse issues to reduce recidivism.

“The hardest part was deciding what I wanted to focus on,” Attari said. “But once I nailed down my topic, each milestone was more straightforward.”

In her own writing experience, Attari said brainstorming was an important step when choosing her topic. She recommends writing down different ideas on a piece of paper and doing some preliminary research on what’s already been written on your topic.

By doing this exercise, you can narrow or broaden your ideas until you’ve found a topic you’re excited about. " Brainstorming is essential when writing a paper and is not a last-minute activity,” Attari said.

How Do I Structure My Thesis Paper?

Thesis papers tend to have a standard format with common sections as the building blocks.

While the structure Attari describes below will work for many theses, it’s important to double-check with your program to see if there are any specific requirements. Writing a thesis for a Master of Fine Arts, for example, might actually look more like a fiction novel.

According to Attari, a thesis paper is often structured with the following major sections:

Introduction

• Literature review
• Methods, results

Now, let’s take a closer look at what each different section should include.

Your introduction is your opportunity to present the topic of your thesis paper. In this section, you can explain why that topic is important. The introduction is also the place to include your thesis statement, which shows your stance in the paper.

Attari said that writing an introduction can be tricky, especially when you're trying to capture your reader’s attention and state your argument.

“I have found that starting with a statement of truth about a topic that pertains to an issue I am writing about typically does the trick,” Attari said. She demonstrated this advice in an example introduction she wrote for a paper on the effects of daylight in Alaska:

In the continental United States, we can always count on the sun rising and setting around the same time each day, but in Alaska, during certain times of the year, the sun rises and does not set for weeks. Research has shown that the sun provides vitamin D and is an essential part of our health, but little is known about how daylight twenty-four hours a day affects the circadian rhythm and sleep.

In the example Attari wrote, she introduces the topic and informs the reader what the paper will cover. Somewhere in her intro, she said she would also include her thesis statement, which might be:

Twenty-four hours of daylight over an extended period does not affect sleep patterns in humans and is not the cause of daytime fatigue in northern Alaska .

Literature Review

In the literature review, you'll look at what information is already out there about your topic. “This is where scholarly articles  about your topic are essential,” said Attari. “These articles will help you find the gap in research that you have identified and will also support your thesis statement."

Telling your reader what research has already been done will help them see how your research fits into the larger conversation. Most university libraries offer databases of scholarly/peer-reviewed articles that can be helpful in your search.

In the methods section of your thesis paper, you get to explain how you learned what you learned. This might include what experiment you conducted as a part of your independent research.

“For instance,” Attari said, “if you are a psychology major and have identified a gap in research on which therapies are effective for anxiety, your methods section would consist of the number of participants, the type of experiment and any other particulars you would use for that experiment.”

In this section, you'll explain the results of your study. For example, building on the psychology example Attari outlined, you might share self-reported anxiety levels for participants trying different kinds of therapies. To help you communicate your results clearly, you might include data, charts, tables or other visualizations.

The discussion section of your thesis paper is where you will analyze and interpret the results you presented in the previous section. This is where you can discuss what your findings really mean or compare them to the research you found in your literature review.

The discussion section is your chance to show why the data you collected matters and how it fits into bigger conversations in your field.

The conclusion of your thesis paper is your opportunity to sum up your argument and leave your reader thinking about why your research matters.

Attari breaks the conclusion down into simple parts. “You restate the original issue and thesis statement, explain the experiment's results and discuss possible next steps for further research,” she said.

Find Your Program

Resources to help write your thesis paper.

While your thesis paper may be based on your independent research, writing it doesn’t have to be a solitary process. Asking for help and using the resources that are available to you can make the process easier.

If you're writing a thesis paper, some resources Chartier encourages you to use are:

• Citation Handbooks: An online citation guide or handbook can help you ensure your citations are correct. APA , MLA and Chicago styles have all published their own guides.
• Citation Generators: There are many citation generator tools that help you to create citations. Some — like RefWorks — even let you directly import citations from library databases as you research.
• Your Library's Website: Many academic and public libraries allow patrons to access resources like databases or FAQs. Some FAQs at the SNHU library that might be helpful in your thesis writing process include “ How do I read a scholarly article? ” or “ What is a research question and how do I develop one? ”

It can also be helpful to check out what coaching or tutoring options are available through your school. At SNHU, for example, the Academic Support Center offers writing and grammar workshops , and students can access 24/7 tutoring and 1:1 sessions with peer tutors, like Attari.

"Students can even submit their papers and receive written feedback... like revisions and editing suggestions," she said.

If you are writing a thesis paper, there are many resources available to you. It's a long paper, but with the right mindset and support, you can successfully navigate the process.

“Pace yourself,” said Chartier. “This is a marathon, not a sprint. Setting smaller goals to get to the big finish line can make the process seem less daunting, and remember to be proud of yourself and celebrate your accomplishment once you’re done. Writing a thesis is no small task, and it’s important work for the scholarly community.”

A degree can change your life. Choose your program  from 200+ SNHU degrees that can take you where you want to go.

Meg Palmer ’18 is a writer and scholar by trade who loves reading, riding her bike and singing in a barbershop quartet. She earned her bachelor’s degree in English, language and literature at Southern New Hampshire University (SNHU) and her master’s degree in writing, rhetoric and discourse at DePaul University (’20). While attending SNHU, she served as the editor-in-chief of the campus student newspaper, The Penmen Press, where she deepened her passion for writing. Meg is an adjunct professor at Johnson and Wales University, where she teaches first year writing, honors composition, and public speaking. Connect with her on LinkedIn .

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CWP: Craft of Prose: Researching the White Paper

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Research the White Paper

Researching the white paper:.

The process of researching and composing a white paper shares some similarities with the kind of research and writing one does for a high school or college research paper. What’s important for writers of white papers to grasp, however, is how much this genre differs from a research paper.  First, the author of a white paper already recognizes that there is a problem to be solved, a decision to be made, and the job of the author is to provide readers with substantive information to help them make some kind of decision--which may include a decision to do more research because major gaps remain. 

Thus, a white paper author would not “brainstorm” a topic. Instead, the white paper author would get busy figuring out how the problem is defined by those who are experiencing it as a problem. Typically that research begins in popular culture--social media, surveys, interviews, newspapers. Once the author has a handle on how the problem is being defined and experienced, its history and its impact, what people in the trenches believe might be the best or worst ways of addressing it, the author then will turn to academic scholarship as well as “grey” literature (more about that later).  Unlike a school research paper, the author does not set out to argue for or against a particular position, and then devote the majority of effort to finding sources to support the selected position.  Instead, the author sets out in good faith to do as much fact-finding as possible, and thus research is likely to present multiple, conflicting, and overlapping perspectives. When people research out of a genuine desire to understand and solve a problem, they listen to every source that may offer helpful information. They will thus have to do much more analysis, synthesis, and sorting of that information, which will often not fall neatly into a “pro” or “con” camp:  Solution A may, for example, solve one part of the problem but exacerbate another part of the problem. Solution C may sound like what everyone wants, but what if it’s built on a set of data that have been criticized by another reliable source?  And so it goes. 

For example, if you are trying to write a white paper on the opioid crisis, you may focus on the value of  providing free, sterilized needles--which do indeed reduce disease, and also provide an opportunity for the health care provider distributing them to offer addiction treatment to the user. However, the free needles are sometimes discarded on the ground, posing a danger to others; or they may be shared; or they may encourage more drug usage. All of those things can be true at once; a reader will want to know about all of these considerations in order to make an informed decision. That is the challenging job of the white paper author.     
 The research you do for your white paper will require that you identify a specific problem, seek popular culture sources to help define the problem, its history, its significance and impact for people affected by it.  You will then delve into academic and grey literature to learn about the way scholars and others with professional expertise answer these same questions. In this way, you will create creating a layered, complex portrait that provides readers with a substantive exploration useful for deliberating and decision-making. You will also likely need to find or create images, including tables, figures, illustrations or photographs, and you will document all of your sources. 

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Speaker 1: Today we will discuss what is perhaps the most single important element of your research paper, the title. The title is the first thing that journal editors and reviewers see when they look at your paper. It is also the only piece of information that fellow researchers will see in a database or search engine query. Therefore, you want to make sure the title captures all of the relevant aspects of your study but does so in a way that is accessible and captivating to readers. Follow these steps to create a perfect title for your paper. First, ask yourself some questions about what your paper seeks to answer and what it accomplishes. What is my paper about? My paper studies how program volume affects outcomes for liver transplant patients on waiting lists. What methods or techniques did I use to perform my study? I employed a case study. What or who was the subject of my study? I studied 60 liver transplant patients on a waiting list in the U.S. aged 20 to 50 years. What were the results? Positive correlation between waiting list volume and poor outcome of transplant procedure. After answering these questions, move on to the second step, which is to identify and list key words and phrases from these responses. Program volume, outcomes, liver transplant patients, waiting lists, case study, 60 liver transplant patients, age 20 to 50 years, positive correlation. These keywords will form the foundation of your title. Once you have identified and listed these keywords, use them to create one long sentence. This study used a case study of 60 liver transplant patients around the U.S. aged 20 to 50 years to assess how the waiting list volume affects the outcome of liver transplantation in patients. These indicate a positive correlation between increased waiting list volume and a negative prognosis after transplant procedure. Next create a working title. Remove elements that make it a complete sentence, but keep everything that is most important to what the study is about. Delete all unnecessary or redundant words. Now let's shift some words around and rephrase it a bit to shorten the length and make it leaner and yet more natural. What you are left with is a case study of 60 liver transplant patients around the U.S. aged 20 to 50 years assessing the impact of waiting list volume on outcome of a transplantation and showing a positive correlation between increased waiting list volume and negative prognosis. This is getting closer to what we want in a title, which is just the most important information. But note that the word count for this working title is still 38 words, whereas the average published journal article title is 16 words or fewer. Therefore we need to eliminate some words and phrases that are not essential to the title. In step 5 you will delete all extra words and phrases and put key words at the beginning and end of your title. Since the number of patients studied and the exact outcome are not the most essential elements of this paper, remove these elements first. In addition, the methods used in a study are not usually the most searched for keywords in databases and represent additional details that you may want to remove to make your title a little leaner. So we are left with assessing the impact of waiting list volume on outcome and prognosis in liver transplantation patients. In this final version you can immediately see how much clearer the title is and what the study is about and what it aims to achieve. And note that the important terms are written at the beginning and the end of the title. Assessing, which is the main action of the study, is at the beginning and liver transplantation patients, the specific subject of the study, is given at the end. This will help a lot with search engine and database queries, meaning that a lot more researchers will find your article once it is published. And if you want to add a subtitle to give more detail about methodology, you can do this by putting this information after a colon. A case study of US adult patients ages 20 to 25. We abide strictly by our word count rule, this may be unnecessary, but every journal has its own standard formatting and style guidelines for titles, so it's a good idea to be aware of these both while writing your title and writing the study itself. So let's review these steps. First, answer some basic questions about your paper. Next, identify and list keywords and phrases from these responses. Third, turn these keywords into a long sentence. Out of this long sentence you can create a working title, deleting any unnecessary and redundant words. Last, delete any extra elements to meet a suitable word count for your title. Note key terms at the beginning and the end, and again you may add a subtitle if it seems necessary or important to this study. Keep these tips in mind when creating your research paper title. Write the title after you've written your paper in abstract. Include all of your paper's essential terms. Keep it short and to the point, about 16 words or fewer is best. Avoid using jargon or abbreviations that will not be understandable by the general reader. Use keywords that closely relate to the content of your paper, and never use a period at the end of your title. Remember, your title is not a sentence. For more tips on how to improve your writing, visit wordvice.com and check out our resources page where you will find a great many helpful articles and videos. And be sure to subscribe to our YouTube channel and social media pages to stay up to date with more excellent academic writing and journal publications content.

APA Style for beginners

Then check out some frequently asked questions:

What is APA Style?

Why use apa style in high school, how do i get started with apa style, what apa style products are available, your help wanted.

APA Style is the most common writing style used in college and career. Its purpose is to promote excellence in communication by helping writers create clear, precise, and inclusive sentences with a straightforward scholarly tone. It addresses areas of writing such as how to

• format a paper so it looks professional;
• credit other people’s words and ideas via citations and references to avoid plagiarism; and
• describe other people with dignity and respect using inclusive, bias-free language.

APA Style is primarily used in the behavioral sciences, which are subjects related to people, such as psychology, education, and nursing. It is also used by students in business, engineering, communications, and other classes. Students use it to write academic essays and research papers in high school and college, and professionals use it to conduct, report, and publish scientific research .

High school students need to learn how to write concisely, precisely, and inclusively so that they are best prepared for college and career. Here are some of the reasons educators have chosen APA Style:

• APA Style is the style of choice for the AP Capstone program, the fastest growing AP course, which requires students to conduct and report independent research.
• APA Style helps students craft written responses on standardized tests such as the SAT and ACT because it teaches students to use a direct and professional tone while avoiding redundancy and flowery language.
• Most college students choose majors that require APA Style or allow APA Style as an option. It can be overwhelming to learn APA Style all at once during the first years of college; starting APA Style instruction in high school sets students up for success.

High school students may also be interested in the TOPSS Competition for High School Psychology Students , an annual competition from the APA Teachers of Psychology in Secondary Schools for high school students to create a short video demonstrating how a psychological topic has the potential to benefit their school and/or local community and improve people’s lives.

Most people are first introduced to APA Style by reading works written in APA Style. The following guides will help with that:

Many people also write research papers or academic essays in APA Style. The following resources will help with that:

Instructors will also benefit from using the following APA Style resources:

In addition to all the free resources on this website, APA publishes several products that provide comprehensive information about APA Style:

The APA Style team is interested in developing additional resources appropriate for a beginner audience. If you have resources you would like to share, or feedback on this topic, please contact the APA Style team . 

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Folklife and Creative Writing: Resources in the American Folklife Center

Introduction.

• Cowboy poetry
• Veterans poetry
• Living Nations, Living Words
• Brazilian Chapbooks
• Veteran Memoirs
• Plays and Novels
• Non-Fiction
• Library Blog Posts and Podcasts
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• Searching the Collection
• Maggie Jones,  Folklife Intern, American Folklife Center
• Melanie Kimball,  Folklife Intern, American Folklife Center
• Meg Nicholas, Folklife Specialist, American Folklife Center

Created:  June 13, 2024

Last Updated:  August 6, 2024

Ask a Librarian: American Folklife Center

American folklife center : ask a librarian.

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This guide provides an introduction to doing research on the topic of creative writing using American Folklife Center collections, and serves as a companion piece to an existing guide on Narrative and Verbal Arts in AFC collections. While the American Folklife Center archive contains primarily unpublished materials, in some cases a collection may include a manuscript or drafts of poems, essays or memoirs that were later published.

The Center's Collection Policy Statement lists nine areas of distinction for its collections, including "Narrative and Verbal Arts." The policy statement has this to say about this area of distinction:

Narrative and Verbal Arts encompass a wide range of genres including stories, myths, legends, jokes, fan literature, memes, proverbs, folk poetry, and cowboy poetry, as well as premier collections of American English regional dialects, plus collections of Gullah (Sea Islands Creole), Caribbean, and French-based Creole dialects.

For the purposes of this guide, "creative writing" encompasses examples of the written word found in AFC archival collections. This may refer to collections of materials written about AFC archival collections, or it may refer to a manuscript item that forms part of a donated collection. The items featured here often cross genres, including poetry, memoirs, and manuscripts for novels, essays and other non-fiction writing. To aid in navigation, collections have been grouped according to the following genres:

• Autobiography and Memoir
• Non-Fiction and Essays

Some collections may feature one creative writing style, while still others contain examples of multiple genres. In the latter case, the collection has been grouped according to the genre which best describes its contents.

In addition to these genre collections, the AFC archive also has items which pertain to the act of writing itself. These include interviews with notable authors which include descriptions of their writing process, as well as an exploration of writing for archival cataloguing.

Some of the collections and resources featured in this guide are available online. Others, including many non-digitized collections of note, require a visit to the American Folklife Center reading room. This guide is not intended to serve as a comprehensive record of all AFC archival collections relevant to this subject.

Writers Found in AFC Collections

• Maya Angelou
• Allen Ginsberg
• Stetson Kennedy
• Arthur Miller
• Toni Morrison
• Zora Neale Hurston

There is a digital presentation of this collection.

Creative Writing in the American Folklife Center

Richard Joseph Heh, author. "My Last Mission" poem written by Richard Joseph Heh. Richard Joseph Heh Collection (AFC 2001/001/87986), Veterans History Project, American Folklife Center, Library of Congress.

Lyntha Scott Eiler, photographer. John Russell, of Montcoal, reading a poem entitled "Keeper of the Forest" at the University of Charleston in Charleston, West Virginia. March 15, 1996. Coal River Folklife Collection (AFC 1999/008), American Folk

Alan Lomax, writer. Draft of unpublished scene from a play about the embodied characters Mind and Matter. Alan Lomax Collection (AFC 2004/004), American Folklife Center, Library of Congress.

Unrealeased outtake footage of an interview with poet Allen Ginsberg . Bruce Jackson and Diane Christian Collection (AFC 2011/009), American Folklife Center, Library of Congress.

Bess Lomax Hawes, writer. Bess's early writing, from 1930. Bess Lomax Hawes Collection (AFC 2014/008), American Folklife Center, Library of Congress

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Peer Reviewed

GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation

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Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research. Our analysis of a selection of questionable GPT-fabricated scientific papers found in Google Scholar shows that many are about applied, often controversial topics susceptible to disinformation: the environment, health, and computing. The resulting enhanced potential for malicious manipulation of society’s evidence base, particularly in politically divisive domains, is a growing concern.

Swedish School of Library and Information Science, University of Borås, Sweden

Department of Arts and Cultural Sciences, Lund University, Sweden

Division of Environmental Communication, Swedish University of Agricultural Sciences, Sweden

Research Questions

• Where are questionable publications produced with generative pre-trained transformers (GPTs) that can be found via Google Scholar published or deposited?
• What are the main characteristics of these publications in relation to predominant subject categories?
• How are these publications spread in the research infrastructure for scholarly communication?
• How is the role of the scholarly communication infrastructure challenged in maintaining public trust in science and evidence through inappropriate use of generative AI?

research note Summary

• A sample of scientific papers with signs of GPT-use found on Google Scholar was retrieved, downloaded, and analyzed using a combination of qualitative coding and descriptive statistics. All papers contained at least one of two common phrases returned by conversational agents that use large language models (LLM) like OpenAI’s ChatGPT. Google Search was then used to determine the extent to which copies of questionable, GPT-fabricated papers were available in various repositories, archives, citation databases, and social media platforms.
• Roughly two-thirds of the retrieved papers were found to have been produced, at least in part, through undisclosed, potentially deceptive use of GPT. The majority (57%) of these questionable papers dealt with policy-relevant subjects (i.e., environment, health, computing), susceptible to influence operations. Most were available in several copies on different domains (e.g., social media, archives, and repositories).
• Two main risks arise from the increasingly common use of GPT to (mass-)produce fake, scientific publications. First, the abundance of fabricated “studies” seeping into all areas of the research infrastructure threatens to overwhelm the scholarly communication system and jeopardize the integrity of the scientific record. A second risk lies in the increased possibility that convincingly scientific-looking content was in fact deceitfully created with AI tools and is also optimized to be retrieved by publicly available academic search engines, particularly Google Scholar. However small, this possibility and awareness of it risks undermining the basis for trust in scientific knowledge and poses serious societal risks.

Implications

The use of ChatGPT to generate text for academic papers has raised concerns about research integrity. Discussion of this phenomenon is ongoing in editorials, commentaries, opinion pieces, and on social media (Bom, 2023; Stokel-Walker, 2024; Thorp, 2023). There are now several lists of papers suspected of GPT misuse, and new papers are constantly being added. 1 See for example Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . While many legitimate uses of GPT for research and academic writing exist (Huang & Tan, 2023; Kitamura, 2023; Lund et al., 2023), its undeclared use—beyond proofreading—has potentially far-reaching implications for both science and society, but especially for their relationship. It, therefore, seems important to extend the discussion to one of the most accessible and well-known intermediaries between science, but also certain types of misinformation, and the public, namely Google Scholar, also in response to the legitimate concerns that the discussion of generative AI and misinformation needs to be more nuanced and empirically substantiated  (Simon et al., 2023).

Google Scholar, https://scholar.google.com , is an easy-to-use academic search engine. It is available for free, and its index is extensive (Gusenbauer & Haddaway, 2020). It is also often touted as a credible source for academic literature and even recommended in library guides, by media and information literacy initiatives, and fact checkers (Tripodi et al., 2023). However, Google Scholar lacks the transparency and adherence to standards that usually characterize citation databases. Instead, Google Scholar uses automated crawlers, like Google’s web search engine (Martín-Martín et al., 2021), and the inclusion criteria are based on primarily technical standards, allowing any individual author—with or without scientific affiliation—to upload papers to be indexed (Google Scholar Help, n.d.). It has been shown that Google Scholar is susceptible to manipulation through citation exploits (Antkare, 2020) and by providing access to fake scientific papers (Dadkhah et al., 2017). A large part of Google Scholar’s index consists of publications from established scientific journals or other forms of quality-controlled, scholarly literature. However, the index also contains a large amount of gray literature, including student papers, working papers, reports, preprint servers, and academic networking sites, as well as material from so-called “questionable” academic journals, including paper mills. The search interface does not offer the possibility to filter the results meaningfully by material type, publication status, or form of quality control, such as limiting the search to peer-reviewed material.

To understand the occurrence of ChatGPT (co-)authored work in Google Scholar’s index, we scraped it for publications, including one of two common ChatGPT responses (see Appendix A) that we encountered on social media and in media reports (DeGeurin, 2024). The results of our descriptive statistical analyses showed that around 62% did not declare the use of GPTs. Most of these GPT-fabricated papers were found in non-indexed journals and working papers, but some cases included research published in mainstream scientific journals and conference proceedings. 2 Indexed journals mean scholarly journals indexed by abstract and citation databases such as Scopus and Web of Science, where the indexation implies journals with high scientific quality. Non-indexed journals are journals that fall outside of this indexation. More than half (57%) of these GPT-fabricated papers concerned policy-relevant subject areas susceptible to influence operations. To avoid increasing the visibility of these publications, we abstained from referencing them in this research note. However, we have made the data available in the Harvard Dataverse repository.

The publications were related to three issue areas—health (14.5%), environment (19.5%) and computing (23%)—with key terms such “healthcare,” “COVID-19,” or “infection”for health-related papers, and “analysis,” “sustainable,” and “global” for environment-related papers. In several cases, the papers had titles that strung together general keywords and buzzwords, thus alluding to very broad and current research. These terms included “biology,” “telehealth,” “climate policy,” “diversity,” and “disrupting,” to name just a few.  While the study’s scope and design did not include a detailed analysis of which parts of the articles included fabricated text, our dataset did contain the surrounding sentences for each occurrence of the suspicious phrases that formed the basis for our search and subsequent selection. Based on that, we can say that the phrases occurred in most sections typically found in scientific publications, including the literature review, methods, conceptual and theoretical frameworks, background, motivation or societal relevance, and even discussion. This was confirmed during the joint coding, where we read and discussed all articles. It became clear that not just the text related to the telltale phrases was created by GPT, but that almost all articles in our sample of questionable articles likely contained traces of GPT-fabricated text everywhere.

Evidence hacking and backfiring effects

Generative pre-trained transformers (GPTs) can be used to produce texts that mimic scientific writing. These texts, when made available online—as we demonstrate—leak into the databases of academic search engines and other parts of the research infrastructure for scholarly communication. This development exacerbates problems that were already present with less sophisticated text generators (Antkare, 2020; Cabanac & Labbé, 2021). Yet, the public release of ChatGPT in 2022, together with the way Google Scholar works, has increased the likelihood of lay people (e.g., media, politicians, patients, students) coming across questionable (or even entirely GPT-fabricated) papers and other problematic research findings. Previous research has emphasized that the ability to determine the value and status of scientific publications for lay people is at stake when misleading articles are passed off as reputable (Haider & Åström, 2017) and that systematic literature reviews risk being compromised (Dadkhah et al., 2017). It has also been highlighted that Google Scholar, in particular, can be and has been exploited for manipulating the evidence base for politically charged issues and to fuel conspiracy narratives (Tripodi et al., 2023). Both concerns are likely to be magnified in the future, increasing the risk of what we suggest calling evidence hacking —the strategic and coordinated malicious manipulation of society’s evidence base.

The authority of quality-controlled research as evidence to support legislation, policy, politics, and other forms of decision-making is undermined by the presence of undeclared GPT-fabricated content in publications professing to be scientific. Due to the large number of archives, repositories, mirror sites, and shadow libraries to which they spread, there is a clear risk that GPT-fabricated, questionable papers will reach audiences even after a possible retraction. There are considerable technical difficulties involved in identifying and tracing computer-fabricated papers (Cabanac & Labbé, 2021; Dadkhah et al., 2023; Jones, 2024), not to mention preventing and curbing their spread and uptake.

However, as the rise of the so-called anti-vaxx movement during the COVID-19 pandemic and the ongoing obstruction and denial of climate change show, retracting erroneous publications often fuels conspiracies and increases the following of these movements rather than stopping them. To illustrate this mechanism, climate deniers frequently question established scientific consensus by pointing to other, supposedly scientific, studies that support their claims. Usually, these are poorly executed, not peer-reviewed, based on obsolete data, or even fraudulent (Dunlap & Brulle, 2020). A similar strategy is successful in the alternative epistemic world of the global anti-vaccination movement (Carrion, 2018) and the persistence of flawed and questionable publications in the scientific record already poses significant problems for health research, policy, and lawmakers, and thus for society as a whole (Littell et al., 2024). Considering that a person’s support for “doing your own research” is associated with increased mistrust in scientific institutions (Chinn & Hasell, 2023), it will be of utmost importance to anticipate and consider such backfiring effects already when designing a technical solution, when suggesting industry or legal regulation, and in the planning of educational measures.

Recommendations

Solutions should be based on simultaneous considerations of technical, educational, and regulatory approaches, as well as incentives, including social ones, across the entire research infrastructure. Paying attention to how these approaches and incentives relate to each other can help identify points and mechanisms for disruption. Recognizing fraudulent academic papers must happen alongside understanding how they reach their audiences and what reasons there might be for some of these papers successfully “sticking around.” A possible way to mitigate some of the risks associated with GPT-fabricated scholarly texts finding their way into academic search engine results would be to provide filtering options for facets such as indexed journals, gray literature, peer-review, and similar on the interface of publicly available academic search engines. Furthermore, evaluation tools for indexed journals 3 Such as LiU Journal CheckUp, https://ep.liu.se/JournalCheckup/default.aspx?lang=eng . could be integrated into the graphical user interfaces and the crawlers of these academic search engines. To enable accountability, it is important that the index (database) of such a search engine is populated according to criteria that are transparent, open to scrutiny, and appropriate to the workings of  science and other forms of academic research. Moreover, considering that Google Scholar has no real competitor, there is a strong case for establishing a freely accessible, non-specialized academic search engine that is not run for commercial reasons but for reasons of public interest. Such measures, together with educational initiatives aimed particularly at policymakers, science communicators, journalists, and other media workers, will be crucial to reducing the possibilities for and effects of malicious manipulation or evidence hacking. It is important not to present this as a technical problem that exists only because of AI text generators but to relate it to the wider concerns in which it is embedded. These range from a largely dysfunctional scholarly publishing system (Haider & Åström, 2017) and academia’s “publish or perish” paradigm to Google’s near-monopoly and ideological battles over the control of information and ultimately knowledge. Any intervention is likely to have systemic effects; these effects need to be considered and assessed in advance and, ideally, followed up on.

Our study focused on a selection of papers that were easily recognizable as fraudulent. We used this relatively small sample as a magnifying glass to examine, delineate, and understand a problem that goes beyond the scope of the sample itself, which however points towards larger concerns that require further investigation. The work of ongoing whistleblowing initiatives 4 Such as Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . , recent media reports of journal closures (Subbaraman, 2024), or GPT-related changes in word use and writing style (Cabanac et al., 2021; Stokel-Walker, 2024) suggest that we only see the tip of the iceberg. There are already more sophisticated cases (Dadkhah et al., 2023) as well as cases involving fabricated images (Gu et al., 2022). Our analysis shows that questionable and potentially manipulative GPT-fabricated papers permeate the research infrastructure and are likely to become a widespread phenomenon. Our findings underline that the risk of fake scientific papers being used to maliciously manipulate evidence (see Dadkhah et al., 2017) must be taken seriously. Manipulation may involve undeclared automatic summaries of texts, inclusion in literature reviews, explicit scientific claims, or the concealment of errors in studies so that they are difficult to detect in peer review. However, the mere possibility of these things happening is a significant risk in its own right that can be strategically exploited and will have ramifications for trust in and perception of science. Society’s methods of evaluating sources and the foundations of media and information literacy are under threat and public trust in science is at risk of further erosion, with far-reaching consequences for society in dealing with information disorders. To address this multifaceted problem, we first need to understand why it exists and proliferates.

Finding 1: 139 GPT-fabricated, questionable papers were found and listed as regular results on the Google Scholar results page. Non-indexed journals dominate.

Most questionable papers we found were in non-indexed journals or were working papers, but we did also find some in established journals, publications, conferences, and repositories. We found a total of 139 papers with a suspected deceptive use of ChatGPT or similar LLM applications (see Table 1). Out of these, 19 were in indexed journals, 89 were in non-indexed journals, 19 were student papers found in university databases, and 12 were working papers (mostly in preprint databases). Table 1 divides these papers into categories. Health and environment papers made up around 34% (47) of the sample. Of these, 66% were present in non-indexed journals.

Finding 2: GPT-fabricated, questionable papers are disseminated online, permeating the research infrastructure for scholarly communication, often in multiple copies. Applied topics with practical implications dominate.

The 20 papers concerning health-related issues are distributed across 20 unique domains, accounting for 46 URLs. The 27 papers dealing with environmental issues can be found across 26 unique domains, accounting for 56 URLs.  Most of the identified papers exist in multiple copies and have already spread to several archives, repositories, and social media. It would be difficult, or impossible, to remove them from the scientific record.

As apparent from Table 2, GPT-fabricated, questionable papers are seeping into most parts of the online research infrastructure for scholarly communication. Platforms on which identified papers have appeared include ResearchGate, ORCiD, Journal of Population Therapeutics and Clinical Pharmacology (JPTCP), Easychair, Frontiers, the Institute of Electrical and Electronics Engineer (IEEE), and X/Twitter. Thus, even if they are retracted from their original source, it will prove very difficult to track, remove, or even just mark them up on other platforms. Moreover, unless regulated, Google Scholar will enable their continued and most likely unlabeled discoverability.

A word rain visualization (Centre for Digital Humanities Uppsala, 2023), which combines word prominences through TF-IDF 5 Term frequency–inverse document frequency , a method for measuring the significance of a word in a document compared to its frequency across all documents in a collection. scores with semantic similarity of the full texts of our sample of GPT-generated articles that fall into the “Environment” and “Health” categories, reflects the two categories in question. However, as can be seen in Figure 1, it also reveals overlap and sub-areas. The y-axis shows word prominences through word positions and font sizes, while the x-axis indicates semantic similarity. In addition to a certain amount of overlap, this reveals sub-areas, which are best described as two distinct events within the word rain. The event on the left bundles terms related to the development and management of health and healthcare with “challenges,” “impact,” and “potential of artificial intelligence”emerging as semantically related terms. Terms related to research infrastructures, environmental, epistemic, and technological concepts are arranged further down in the same event (e.g., “system,” “climate,” “understanding,” “knowledge,” “learning,” “education,” “sustainable”). A second distinct event further to the right bundles terms associated with fish farming and aquatic medicinal plants, highlighting the presence of an aquaculture cluster.  Here, the prominence of groups of terms such as “used,” “model,” “-based,” and “traditional” suggests the presence of applied research on these topics. The two events making up the word rain visualization, are linked by a less dominant but overlapping cluster of terms related to “energy” and “water.”

The bar chart of the terms in the paper subset (see Figure 2) complements the word rain visualization by depicting the most prominent terms in the full texts along the y-axis. Here, word prominences across health and environment papers are arranged descendingly, where values outside parentheses are TF-IDF values (relative frequencies) and values inside parentheses are raw term frequencies (absolute frequencies).

Finding 3: Google Scholar presents results from quality-controlled and non-controlled citation databases on the same interface, providing unfiltered access to GPT-fabricated questionable papers.

Google Scholar’s central position in the publicly accessible scholarly communication infrastructure, as well as its lack of standards, transparency, and accountability in terms of inclusion criteria, has potentially serious implications for public trust in science. This is likely to exacerbate the already-known potential to exploit Google Scholar for evidence hacking (Tripodi et al., 2023) and will have implications for any attempts to retract or remove fraudulent papers from their original publication venues. Any solution must consider the entirety of the research infrastructure for scholarly communication and the interplay of different actors, interests, and incentives.

We searched and scraped Google Scholar using the Python library Scholarly (Cholewiak et al., 2023) for papers that included specific phrases known to be common responses from ChatGPT and similar applications with the same underlying model (GPT3.5 or GPT4): “as of my last knowledge update” and/or “I don’t have access to real-time data” (see Appendix A). This facilitated the identification of papers that likely used generative AI to produce text, resulting in 227 retrieved papers. The papers’ bibliographic information was automatically added to a spreadsheet and downloaded into Zotero. 6 An open-source reference manager, https://zotero.org .

We employed multiple coding (Barbour, 2001) to classify the papers based on their content. First, we jointly assessed whether the paper was suspected of fraudulent use of ChatGPT (or similar) based on how the text was integrated into the papers and whether the paper was presented as original research output or the AI tool’s role was acknowledged. Second, in analyzing the content of the papers, we continued the multiple coding by classifying the fraudulent papers into four categories identified during an initial round of analysis—health, environment, computing, and others—and then determining which subjects were most affected by this issue (see Table 1). Out of the 227 retrieved papers, 88 papers were written with legitimate and/or declared use of GPTs (i.e., false positives, which were excluded from further analysis), and 139 papers were written with undeclared and/or fraudulent use (i.e., true positives, which were included in further analysis). The multiple coding was conducted jointly by all authors of the present article, who collaboratively coded and cross-checked each other’s interpretation of the data simultaneously in a shared spreadsheet file. This was done to single out coding discrepancies and settle coding disagreements, which in turn ensured methodological thoroughness and analytical consensus (see Barbour, 2001). Redoing the category coding later based on our established coding schedule, we achieved an intercoder reliability (Cohen’s kappa) of 0.806 after eradicating obvious differences.

The ranking algorithm of Google Scholar prioritizes highly cited and older publications (Martín-Martín et al., 2016). Therefore, the position of the articles on the search engine results pages was not particularly informative, considering the relatively small number of results in combination with the recency of the publications. Only the query “as of my last knowledge update” had more than two search engine result pages. On those, questionable articles with undeclared use of GPTs were evenly distributed across all result pages (min: 4, max: 9, mode: 8), with the proportion of undeclared use being slightly higher on average on later search result pages.

To understand how the papers making fraudulent use of generative AI were disseminated online, we programmatically searched for the paper titles (with exact string matching) in Google Search from our local IP address (see Appendix B) using the googlesearch – python library(Vikramaditya, 2020). We manually verified each search result to filter out false positives—results that were not related to the paper—and then compiled the most prominent URLs by field. This enabled the identification of other platforms through which the papers had been spread. We did not, however, investigate whether copies had spread into SciHub or other shadow libraries, or if they were referenced in Wikipedia.

We used descriptive statistics to count the prevalence of the number of GPT-fabricated papers across topics and venues and top domains by subject. The pandas software library for the Python programming language (The pandas development team, 2024) was used for this part of the analysis. Based on the multiple coding, paper occurrences were counted in relation to their categories, divided into indexed journals, non-indexed journals, student papers, and working papers. The schemes, subdomains, and subdirectories of the URL strings were filtered out while top-level domains and second-level domains were kept, which led to normalizing domain names. This, in turn, allowed the counting of domain frequencies in the environment and health categories. To distinguish word prominences and meanings in the environment and health-related GPT-fabricated questionable papers, a semantically-aware word cloud visualization was produced through the use of a word rain (Centre for Digital Humanities Uppsala, 2023) for full-text versions of the papers. Font size and y-axis positions indicate word prominences through TF-IDF scores for the environment and health papers (also visualized in a separate bar chart with raw term frequencies in parentheses), and words are positioned along the x-axis to reflect semantic similarity (Skeppstedt et al., 2024), with an English Word2vec skip gram model space (Fares et al., 2017). An English stop word list was used, along with a manually produced list including terms such as “https,” “volume,” or “years.”

• Artificial Intelligence
• / Search engines

Cite this Essay

Haider, J., Söderström, K. R., Ekström, B., & Rödl, M. (2024). GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation. Harvard Kennedy School (HKS) Misinformation Review . https://doi.org/10.37016/mr-2020-156

• / Appendix B

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This research has been supported by Mistra, the Swedish Foundation for Strategic Environmental Research, through the research program Mistra Environmental Communication (Haider, Ekström, Rödl) and the Marcus and Amalia Wallenberg Foundation [2020.0004] (Söderström).

Competing Interests

The authors declare no competing interests.

The research described in this article was carried out under Swedish legislation. According to the relevant EU and Swedish legislation (2003:460) on the ethical review of research involving humans (“Ethical Review Act”), the research reported on here is not subject to authorization by the Swedish Ethical Review Authority (“etikprövningsmyndigheten”) (SRC, 2017).

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited.

Data Availability

All data needed to replicate this study are available at the Harvard Dataverse: https://doi.org/10.7910/DVN/WUVD8X

Acknowledgements

The authors wish to thank two anonymous reviewers for their valuable comments on the article manuscript as well as the editorial group of Harvard Kennedy School (HKS) Misinformation Review for their thoughtful feedback and input.

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Title: writing in the margins: better inference pattern for long context retrieval.

Abstract: In this paper, we introduce Writing in the Margins (WiM), a new inference pattern for Large Language Models designed to optimize the handling of long input sequences in retrieval-oriented tasks. This approach leverages the chunked prefill of the key-value cache to perform segment-wise inference, which enables efficient processing of extensive contexts along with the generation and classification of intermediate information ("margins") that guide the model towards specific tasks. This method increases computational overhead marginally while significantly enhancing the performance of off-the-shelf models without the need for fine-tuning. Specifically, we observe that WiM provides an average enhancement of 7.5% in accuracy for reasoning skills (HotpotQA, MultiHop-RAG) and more than a 30.0% increase in the F1-score for aggregation tasks (CWE). Additionally, we show how the proposed pattern fits into an interactive retrieval design that provides end-users with ongoing updates about the progress of context processing, and pinpoints the integration of relevant information into the final response. We release our implementation of WiM using Hugging Face Transformers library at this https URL .

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• 30 August 2024

Researchers built an ‘AI Scientist’ — what can it do?

• Davide Castelvecchi

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Could science be fully automated? A team of machine-learning researchers has now tried.

‘AI Scientist’, created by a team at Tokyo company Sakana AI and at academic labs in Canada and the United Kingdom, performs the full cycle of research from reading the existing literature on a problem and formulating hypothesis for new developments to trying out solutions and writing a paper. AI Scientist even does some of the job of peer reviewers and evaluates its own results.

AI Scientist joins a slew of efforts to create AI agents that have automated at least parts of the scientific process. “To my knowledge, no one has yet done the total scientific community, all in one system,” says AI Scientist co-creator Cong Lu, a machine-learning researcher at the University of British Columbia in Vancouver, Canada. The results 1 were posted on the arXiv preprint server this month.

“It’s impressive that they’ve done this end-to-end,” says Jevin West, a computational social scientist at the University of Washington in Seattle. “And I think we should be playing around with these ideas, because there could be potential for helping science.”

The output is not earth-shattering so far, and the system can only do research in the field of machine learning itself. In particular, AI Scientist is lacking what most scientists would consider the crucial part of doing science — the ability to do laboratory work . “There’s still a lot of work to go from AI that makes a hypothesis to implementing that in a robot scientist,” says Gerbrand Ceder, a materials scientist at Lawrence Berkeley National Laboratory and the University of California, Berkeley. Still, Ceder adds, “If you look into the future, I have zero doubt in mind that this is where much of science will go.”

Automated experiments

AI Scientist is based on a large language model (LLM). Using a paper that describes a machine learning algorithm as template, it starts from searching the literature for similar work. The team then employed the technique called evolutionary computation, which is inspired by the mutations and natural selection of Darwinian evolution. It proceeds in steps, applying small, random changes to an algorithm and selecting the ones that provide an improvement in efficiency.

To do so, AI Scientist conducts its own ‘experiments’ by running the algorithms and measuring how they perform. At the end, it produces a paper, and evaluates it in a sort of automated peer review. After ‘augmenting the literature’ this way, the algorithm can then start the cycle again, building on its own results.

The authors admit that the papers AI Scientists produced contained only incremental developments. Some other researchers were scathing in their comments on social media. “As an editor of a journal, I would likely desk-reject them. As a reviewer, I would reject them,” said one commenter on the website Hacker News.

West also says that the authors took a reductive view of how researchers learn about the current state of their field. A lot of what they know comes from other forms of communication, such as going to conferences or chatting to colleagues at the water cooler. “Science is more than a pile of papers,” says West. “You can have a 5-minute conversation that will be better than a 5-hour study of the literature.”

West’s colleague Shahan Memon agrees — but both West and Memon praise the authors for having made their code and results fully open. This has enabled them to analyze the AI Scientist’s results. They’ve found, for example, that it has a “popularity bias” in the choice of earlier papers it lists as references, skirting towards those with high citation counts. Memon and West say they are also looking into measuring whether AI Scientist’s choices were the most relevant ones.

Repetitive tasks

AI Scientist is, of course, not the first attempt at automating at least various parts of the job of a researcher: the dream of automating scientific discovery is as old as artificial intelligence itself — dating back to the 1950s, says Tom Hope, a computer scientist at the Allen Institute for AI based in Jerusalem. Already a decade ago, for example, the Automatic Statistician 2 was able to analyse sets of data and write up its own papers. And Ceder and his colleagues have even automated some bench work: the ‘ robot chemist ’ they unveiled last year can synthesize new materials and experiment with them 3 .

Hope says that current LLMs “are not able to formulate novel and useful scientific directions beyond basic superficial combinations of buzzwords”. Still, Ceder says that even if AI won’t able to do the more creative part of the work any time soon, it could still automate a lot of the more repetitive aspects of research. “At the low level, you’re trying to analyse what something is, how something responds. That’s not the creative part of science, but it’s 90% of what we do.” Lu says he got a similar feedback from a lot of other researchers, too. “People will say, I have 100 ideas that I don’t have time for. Get the AI Scientist to do those.”

Lu says that to broaden AI Scientist’s capabilities — even to abstract fields beyond machine learning, such as pure mathematics — it might need to include other techniques beyond language models. Recent results on solving maths problems by Google Deep Mind, for example, have shown the power of combining LLMs with techniques of ‘symbolic’ AI, which build logical rules into a system rather than merely relying on it learning from statistical patterns in data. But the current iteration is but a start, he says. “We really believe this is the GPT-1 of AI science,” he says, referring to an early large language model by OpenAI in San Francisco, California.

The results feed into a debate that is at the top of many researchers’ concerns these days, says West. “All my colleagues in different sciences are trying to figure out, where does AI fit in in what we do? It does force us to think what is science in the twenty-first century — what it could be, what it is, what it is not,” he says.

doi: https://doi.org/10.1038/d41586-024-02842-3

Lu, C., Lu, C., Lange, R. T., Foerster, J., Clune, J. & Ha, D. Preprint at arXiv https://arxiv.org/abs/2408.06292 (2024).

Ghahramani, Z. Nature 521 , 452–459 (2015).

Google Scholar  

Szymanski, N. J. et al. Nature 624 , 86–91 (2023).

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• As Ozempic’s Popularity Soars, Here’s What to Know About Semaglutide and Weight Loss JAMA Medical News & Perspectives May 16, 2023 This Medical News article discusses chronic weight management with semaglutide, sold under the brand names Ozempic and Wegovy. Melissa Suran, PhD, MSJ
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• What to Know About Wegovy’s Rare but Serious Adverse Effects JAMA Medical News & Perspectives December 12, 2023 This Medical News article discusses Wegovy, Ozempic, and other GLP-1 receptor agonists used for weight management and type 2 diabetes. Kate Ruder, MSJ
• GLP-1 Receptor Agonists and Gastrointestinal Adverse Events—Reply JAMA Comment & Response March 12, 2024 Ramin Rezaeianzadeh, BSc; Mohit Sodhi, MSc; Mahyar Etminan, PharmD, MSc
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• GLP-1 Receptor Agonist Use and Risk of Postoperative Complications JAMA Research Letter May 21, 2024 This cohort study evaluates the risk of postoperative respiratory complications among patients with diabetes undergoing surgery who had vs those who had not a prescription fill for glucagon-like peptide 1 receptor agonists. Anjali A. Dixit, MD, MPH; Brian T. Bateman, MD, MS; Mary T. Hawn, MD, MPH; Michelle C. Odden, PhD; Eric C. Sun, MD, PhD
• Glucagon-Like Peptide-1 Receptor Agonist Use and Risk of Gallbladder and Biliary Diseases JAMA Internal Medicine Original Investigation May 1, 2022 This systematic review and meta-analysis of 76 randomized clinical trials examines the effects of glucagon-like peptide-1 receptor agonist use on the risk of gallbladder and biliary diseases. Liyun He, MM; Jialu Wang, MM; Fan Ping, MD; Na Yang, MM; Jingyue Huang, MM; Yuxiu Li, MD; Lingling Xu, MD; Wei Li, MD; Huabing Zhang, MD
• Cholecystitis Associated With the Use of Glucagon-Like Peptide-1 Receptor Agonists JAMA Internal Medicine Research Letter October 1, 2022 This case series identifies cases reported in the US Food and Drug Administration Adverse Event Reporting System of acute cholecystitis associated with use of glucagon-like peptide-1 receptor agonists that did not have gallbladder disease warnings in their labeling. Daniel Woronow, MD; Christine Chamberlain, PharmD; Ali Niak, MD; Mark Avigan, MDCM; Monika Houstoun, PharmD, MPH; Cindy Kortepeter, PharmD

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Sodhi M , Rezaeianzadeh R , Kezouh A , Etminan M. Risk of Gastrointestinal Adverse Events Associated With Glucagon-Like Peptide-1 Receptor Agonists for Weight Loss. JAMA. 2023;330(18):1795–1797. doi:10.1001/jama.2023.19574

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Risk of Gastrointestinal Adverse Events Associated With Glucagon-Like Peptide-1 Receptor Agonists for Weight Loss

• 1 Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
• 2 StatExpert Ltd, Laval, Quebec, Canada
• 3 Department of Ophthalmology and Visual Sciences and Medicine, University of British Columbia, Vancouver, Canada
• Medical News & Perspectives As Ozempic’s Popularity Soars, Here’s What to Know About Semaglutide and Weight Loss Melissa Suran, PhD, MSJ JAMA
• Special Communication Patents and Regulatory Exclusivities on GLP-1 Receptor Agonists Rasha Alhiary, PharmD; Aaron S. Kesselheim, MD, JD, MPH; Sarah Gabriele, LLM, MBE; Reed F. Beall, PhD; S. Sean Tu, JD, PhD; William B. Feldman, MD, DPhil, MPH JAMA
• Medical News & Perspectives What to Know About Wegovy’s Rare but Serious Adverse Effects Kate Ruder, MSJ JAMA
• Comment & Response GLP-1 Receptor Agonists and Gastrointestinal Adverse Events—Reply Ramin Rezaeianzadeh, BSc; Mohit Sodhi, MSc; Mahyar Etminan, PharmD, MSc JAMA
• Comment & Response GLP-1 Receptor Agonists and Gastrointestinal Adverse Events Karine Suissa, PhD; Sara J. Cromer, MD; Elisabetta Patorno, MD, DrPH JAMA
• Research Letter GLP-1 Receptor Agonist Use and Risk of Postoperative Complications Anjali A. Dixit, MD, MPH; Brian T. Bateman, MD, MS; Mary T. Hawn, MD, MPH; Michelle C. Odden, PhD; Eric C. Sun, MD, PhD JAMA
• Original Investigation Glucagon-Like Peptide-1 Receptor Agonist Use and Risk of Gallbladder and Biliary Diseases Liyun He, MM; Jialu Wang, MM; Fan Ping, MD; Na Yang, MM; Jingyue Huang, MM; Yuxiu Li, MD; Lingling Xu, MD; Wei Li, MD; Huabing Zhang, MD JAMA Internal Medicine
• Research Letter Cholecystitis Associated With the Use of Glucagon-Like Peptide-1 Receptor Agonists Daniel Woronow, MD; Christine Chamberlain, PharmD; Ali Niak, MD; Mark Avigan, MDCM; Monika Houstoun, PharmD, MPH; Cindy Kortepeter, PharmD JAMA Internal Medicine

Glucagon-like peptide 1 (GLP-1) agonists are medications approved for treatment of diabetes that recently have also been used off label for weight loss. 1 Studies have found increased risks of gastrointestinal adverse events (biliary disease, 2 pancreatitis, 3 bowel obstruction, 4 and gastroparesis 5 ) in patients with diabetes. 2 - 5 Because such patients have higher baseline risk for gastrointestinal adverse events, risk in patients taking these drugs for other indications may differ. Randomized trials examining efficacy of GLP-1 agonists for weight loss were not designed to capture these events 2 due to small sample sizes and short follow-up. We examined gastrointestinal adverse events associated with GLP-1 agonists used for weight loss in a clinical setting.

We used a random sample of 16 million patients (2006-2020) from the PharMetrics Plus for Academics database (IQVIA), a large health claims database that captures 93% of all outpatient prescriptions and physician diagnoses in the US through the International Classification of Diseases, Ninth Revision (ICD-9) or ICD-10. In our cohort study, we included new users of semaglutide or liraglutide, 2 main GLP-1 agonists, and the active comparator bupropion-naltrexone, a weight loss agent unrelated to GLP-1 agonists. Because semaglutide was marketed for weight loss after the study period (2021), we ensured all GLP-1 agonist and bupropion-naltrexone users had an obesity code in the 90 days prior or up to 30 days after cohort entry, excluding those with a diabetes or antidiabetic drug code.

Patients were observed from first prescription of a study drug to first mutually exclusive incidence (defined as first ICD-9 or ICD-10 code) of biliary disease (including cholecystitis, cholelithiasis, and choledocholithiasis), pancreatitis (including gallstone pancreatitis), bowel obstruction, or gastroparesis (defined as use of a code or a promotility agent). They were followed up to the end of the study period (June 2020) or censored during a switch. Hazard ratios (HRs) from a Cox model were adjusted for age, sex, alcohol use, smoking, hyperlipidemia, abdominal surgery in the previous 30 days, and geographic location, which were identified as common cause variables or risk factors. 6 Two sensitivity analyses were undertaken, one excluding hyperlipidemia (because more semaglutide users had hyperlipidemia) and another including patients without diabetes regardless of having an obesity code. Due to absence of data on body mass index (BMI), the E-value was used to examine how strong unmeasured confounding would need to be to negate observed results, with E-value HRs of at least 2 indicating BMI is unlikely to change study results. Statistical significance was defined as 2-sided 95% CI that did not cross 1. Analyses were performed using SAS version 9.4. Ethics approval was obtained by the University of British Columbia’s clinical research ethics board with a waiver of informed consent.

Our cohort included 4144 liraglutide, 613 semaglutide, and 654 bupropion-naltrexone users. Incidence rates for the 4 outcomes were elevated among GLP-1 agonists compared with bupropion-naltrexone users ( Table 1 ). For example, incidence of biliary disease (per 1000 person-years) was 11.7 for semaglutide, 18.6 for liraglutide, and 12.6 for bupropion-naltrexone and 4.6, 7.9, and 1.0, respectively, for pancreatitis.

Use of GLP-1 agonists compared with bupropion-naltrexone was associated with increased risk of pancreatitis (adjusted HR, 9.09 [95% CI, 1.25-66.00]), bowel obstruction (HR, 4.22 [95% CI, 1.02-17.40]), and gastroparesis (HR, 3.67 [95% CI, 1.15-11.90) but not biliary disease (HR, 1.50 [95% CI, 0.89-2.53]). Exclusion of hyperlipidemia from the analysis did not change the results ( Table 2 ). Inclusion of GLP-1 agonists regardless of history of obesity reduced HRs and narrowed CIs but did not change the significance of the results ( Table 2 ). E-value HRs did not suggest potential confounding by BMI.

This study found that use of GLP-1 agonists for weight loss compared with use of bupropion-naltrexone was associated with increased risk of pancreatitis, gastroparesis, and bowel obstruction but not biliary disease.

Given the wide use of these drugs, these adverse events, although rare, must be considered by patients who are contemplating using the drugs for weight loss because the risk-benefit calculus for this group might differ from that of those who use them for diabetes. Limitations include that although all GLP-1 agonist users had a record for obesity without diabetes, whether GLP-1 agonists were all used for weight loss is uncertain.

Accepted for Publication: September 11, 2023.

Published Online: October 5, 2023. doi:10.1001/jama.2023.19574

Correction: This article was corrected on December 21, 2023, to update the full name of the database used.

Corresponding Author: Mahyar Etminan, PharmD, MSc, Faculty of Medicine, Departments of Ophthalmology and Visual Sciences and Medicine, The Eye Care Center, University of British Columbia, 2550 Willow St, Room 323, Vancouver, BC V5Z 3N9, Canada ( [email protected] ).

Author Contributions: Dr Etminan had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Sodhi, Rezaeianzadeh, Etminan.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Sodhi, Rezaeianzadeh, Etminan.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Kezouh.

Obtained funding: Etminan.

Administrative, technical, or material support: Sodhi.

Supervision: Etminan.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was funded by internal research funds from the Department of Ophthalmology and Visual Sciences, University of British Columbia.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement .

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U.S. AI Safety Institute Signs Agreements Regarding AI Safety Research, Testing and Evaluation With Anthropic and OpenAI

These first-of-their-kind agreements between the u.s. government and industry will help advance safe and trustworthy ai innovation for all..

GAITHERSBURG, Md. — Today, the U.S. Artificial Intelligence Safety Institute at the U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) announced agreements that enable formal collaboration on AI safety research, testing and evaluation with both Anthropic and OpenAI.

Each company’s Memorandum of Understanding establishes the framework for the U.S. AI Safety Institute to receive access to major new models from each company prior to and following their public release. The agreements will enable collaborative research on how to evaluate capabilities and safety risks, as well as methods to mitigate those risks. 

“Safety is essential to fueling breakthrough technological innovation. With these agreements in place, we look forward to beginning our technical collaborations with Anthropic and OpenAI to advance the science of AI safety,” said Elizabeth Kelly, director of the U.S. AI Safety Institute. “These agreements are just the start, but they are an important milestone as we work to help responsibly steward the future of AI.”

Additionally, the U.S. AI Safety Institute plans to provide feedback to Anthropic and OpenAI on potential safety improvements to their models, in close collaboration with its partners at the U.K. AI Safety Institute. 

The U.S. AI Safety Institute builds on NIST’s more than 120-year legacy of advancing measurement science, technology, standards and related tools. Evaluations under these agreements will further NIST’s work on AI by facilitating deep collaboration and exploratory research on advanced AI systems across a range of risk areas.

Evaluations conducted pursuant to these agreements will help advance the safe, secure and trustworthy development and use of AI by building on the Biden-Harris administration’s Executive Order on AI and the voluntary commitments made to the administration by leading AI model developers.

About the U.S. AI Safety Institute

The U.S. AI Safety Institute , located within the Department of Commerce at the National Institute of Standards and Technology (NIST), was established following the Biden-Harris administration’s 2023 Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence to advance the science of AI safety and address the risks posed by advanced AI systems. It is tasked with developing the testing, evaluations and guidelines that will help accelerate safe AI innovation here in the United States and around the world. 

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We Know Mental Health Affects Physical Health. Why Don’t We Act Like It?

By Helen Ouyang

Dr. Ouyang is an emergency physician and an associate professor at Columbia University.

“Do you think my symptoms could be from stress?” This is a question more and more of my patients have been asking me when seeking care for problems such as stubborn colds and aching backs.

For other patients who don’t first raise the idea, when I propose that their mental health could be worsening their physical symptoms, they no longer bristle at that suggestion. An idea that was once dismissed as New Age fluff is now being embraced.

Our health care system, however, has yet to catch up to this connection. While it promotes preventive care, the emphasis has largely been on physical health, overlooking the significant influence of mental well-being on our overall health and vulnerability to illness. For people to truly be healthy, modern medicine must prioritize the prevention of mental ailments alongside physical ones.

It wasn’t long ago that even hinting at the notion that a relationship exists between our mental and our physical health would inevitably seem to frustrate, even infuriate, patients, as though their suffering weren’t real. And for good reason. Women have endured the dismissal of their symptoms for centuries, resulting in missed and delayed diagnoses, even death. Pain in Black patients is often undertreated . Doctors in the past would offhandedly call certain symptoms supratentorial — meaning “in one’s head.”

But our understanding is evolving as a growing stream of research challenges old assumptions. Psychological disorders such as stress, depression and loneliness are now known to be associated with impaired immune defenses , leading to increased infections and weakened responses to vaccines . Chronic stress can disrupt our gut function, slow our wound healing and age our cells.

The mind’s potential to unravel the body goes even further: People with symptoms of depression have a greater risk of developing coronary artery disease. Among patients who already have heart failure, those struggling with loneliness have a significantly higher death rate . Job stress has been linked to strokes.

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Exploring Senior High School Students' Academic Writing Difficulties: Towards an Academic Writing Model

2020, IOER International Multidisciplinary Research Journal

Undeniably, writing is an indispensable skill in different contexts of life. It is one of the pivotal components of education. With the advent of the K-12 Basic Education Curriculum, students are confronted with different academic writing tasks through the English for Academic and Professional Purposes and Practical Research courses. Despite the efforts of the educational sector to improve students' writing skills, many students still experience difficulties in academic writing. Therefore, there is a need to fill the gap and advance an in-depth understanding of students' academic writing difficulties. In order to satisfy the said aim, this Qualitative study grounds on Flower and Hayes' (1981) Cognitive Process Theory of Writing. Focus group discussion of 14 purposively selected Grade 11 Senior High School students was carried out. The results expound on the different Academic Writing difficulties encountered by the participants in terms of the Task Environment, Writers' Long-Term Memory and Writing Process. A model for teaching academic writing was formulated anchored on the identified difficulties. The model will benefit the teachers as it may serve as a guide in more effective and efficient teaching of academic writing.

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Identifying Problems of Undergraduate Students in Writing Research: Basis for Scaffolding

15 Pages Posted: 15 Jun 2018 Last revised: 17 Jul 2018

Jovita Villanueva

Bulacan state university.

Date Written: January 8, 2017

The paper identified the various areas in research writing where undergraduate students of Bulacan State University are having difficulties more particularly on the parts of research from writing chapter 1 to chapter 5. The researcher made used of the descriptive survey method in which the questionnaire is the main tool in data gathering. Furthermore, the researcher made use of a self –constructed research questionnaire using Likert Scale of five and the instrument was duly validated by experts in the field of research. Research instrument also underwent test of validity and reliability prior to distribution to respondents. Respondents of the study were students of college of education of Bulacan State University taking research as a subject and enrolled in the 2nd semester of 2016-2017. Data gathered were tallied, tabulated, treated and analyzed using the SPSS and interpreted accordingly. Based on the results of the study, an intervention program will be proposed to help addressed the problems of undergraduate students in research writing.

Keywords: Research, Writing Research, Problems of Undergraduate Student, Scaffolding for Students

JEL Classification: I23

Suggested Citation: Suggested Citation

Jovita Villanueva (Contact Author)

Philippines

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Undergraduate Students’ Difficulties In Writing A Research Proposal: A Case Study

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