Evidence map›Paper›PMID 40163844›Full record

ArticleJournal of medical Internet research2025

Endometriosis Communities on Reddit: Quantitative Analysis.

Federica Bologna, Rosamond Thalken, Kristen Pepin, Matthew Wilkens

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Federica BolognaDepartment of Information Science, Cornell University, Ithaca, NY, United States.ORCID 0000-0002-3845-8266
Rosamond ThalkenDepartment of Information Science, Cornell University, Ithaca, NY, United States.ORCID 0000-0001-6290-074X
Kristen PepinDepartment of Obstetrics and Gynecology, Weill Cornell Medicine, New York, NY, United States.ORCID 0000-0003-4370-3593
Matthew WilkensDepartment of Information Science, Cornell University, Ithaca, NY, United States.ORCID 0000-0001-6749-9318

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEndometriosis is a chronic condition that affects 10% of the people with a uterus. Due to the complex social and psychological impacts caused by this condition, people with endometriosis often turn to online health communities (OHCs) for support.

objectivePrior work identifies a lack of large-scale analyses of the experiences of patients with endometriosis and of OHCs. This study aims to fill this gap by investigating aspects of the condition and aggregate user needs that emerge from 2 endometriosis OHCs, r/Endo and r/endometriosis.

methodsWe used latent Dirichlet allocation topic modeling, an unsupervised machine learning method, to extract the subject matter ("topic") of >30,000 posts and >300,000 comments. In addition to latent Dirichlet allocation, we leveraged supervised classification. Specifically, we fine-tuned a series of the DistilBERT models to identify the people and relationships (personas) a post mentions as well as the type of support that the post seeks (intent). Combining the results of these 2 methods, we identified associations between a post's topic, the personas mentioned, and the post's intent.

resultsThe most discussed topics in posts were medical stories, medical appointments, sharing symptoms, menstruation, and empathy. Through the combination of the results from topic modeling and supervised classification, we found that when discussing medical appointments, users were more likely to mention the endometriosis OHCs than medical professionals. Medical professional was the least likely of any persona to be associated with empathy. Posts that mentioned partner or family were likely to discuss topics from the life issues category, particularly fertility. Users sought experiential knowledge regarding treatments and health care processes, and they also wished to vent and establish emotional connections about the life-altering aspects of the condition.

conclusionsWe conclude that members of the OHCs need greater empathy within clinical settings, easier access to appointments, more information on care pathways, and more support for their loved ones. Endometriosis OHCs currently fulfill some of these needs as they provide members with a space where they can receive validation, discuss care pathways, and learn to manage symptoms. This study demonstrates the value of quantitative analyses of OHCs. Computational analyses can support and extend findings from small-scale studies about patient experiences and provide insights into hard-to-reach groups. Finally, we provide recommendations for clinical practice and medical training programs.

Indexed as

EndometriosisSocial MediaFemaleHumansMachine Learningchronic diseasecommunity networksconsumer health informationinformation scienceinternetonline health communitiespatient-centered careself-help groupssocial support

Identifiers

PMID40163844
PMCPMC11997530

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.