Comparing Literature- and Subreddit-Derived Laboratory Values in Polycystic Ovary Syndrome (PCOS): Validation of Clinical Data Posted on PCOS Reddit Forums
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Abstract
Background: Polycystic ovary syndrome (PCOS) is a heterogeneous condition that affects 4% to 21% of people with ovaries. Inaccessibility or dissatisfaction with clinical treatment for PCOS has led to some individuals with the condition discussing their experiences in specialized web-based forums. Objective: This study explores the feasibility of using such web-based forums for clinical research purposes by gathering and analyzing laboratory test results posted in an active PCOS forum, specifically the PCOS subreddit hosted on Reddit. Methods: We gathered around 45,000 posts from the PCOS subreddit. A random subset of 5000 posts was manually read, and the presence of laboratory test results was labeled. These labeled posts were used to train a machine learning model to identify which of the remaining posts contained laboratory results. The laboratory results were extracted manually from the identified posts. These self-reported laboratory test results were compared with values in the published literature to assess whether the results were concordant with researcher-published values for PCOS cohorts. A total of 10 papers were chosen to represent published PCOS literature, with selection criteria including the Rotterdam diagnostic criteria for PCOS, a publication date within the last 20 years, and at least 50 participants with PCOS. Results: Overall, the general trends observed in the laboratory test results from the PCOS web-based forum were consistent with clinically reported PCOS. A number of results, such as follicle stimulating hormone, fasting insulin, and anti-Mullerian hormone, were concordant with published values for patients with PCOS. The high consistency of these results among the literature and when compared to the subreddit suggests that follicle stimulating hormone, fasting insulin, and anti-Mullerian hormone are more consistent across PCOS phenotypes than other test results. Some results, such as testosterone, sex hormone–binding globulin, and homeostasis model assessment–estimated insulin resistance index, were between those of PCOS literature values and normal values, as defined by clinical testing limits. Interestingly, other results, including dehydroepiandrosterone sulfate, luteinizing hormone, and fasting glucose, appeared to be slightly more dysregulated than those reported in the literature. Conclusions: The differences between the forum-posted results and those published in the literature may be due to the selection process in clinical studies and the possibility that the forum disproportionally describes PCOS phenotypes that are less likely to be alleviated with medical intervention. However, the degree of concordance in most laboratory test values implied that the PCOS web-based forum participants were representative of research-identified PCOS cohorts. This validation of the PCOS subreddit grants the possibility for more research into the contents of the subreddit and the idea of undertaking similar research using the contents of other medical internet forums.
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The majority of machine learning studies originated from India (n = 5), while three originated from the USA, and one from New Zealand.
“The majority of these studies originated from India (n = 5) [63,64,65,66,88], while three originated from the USA (n = 3) [67,87,89], and one from New Zealand [76].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [76]
Citation [76] is attached to the New Zealand study, not the India or USA counts. The Methods > Data Collection and Cleaning passage confirms that this paper used machine learning to analyze PCOS subreddit posts, but the passages in our copy do not establish that the study originated in New Zealand. (LLM, conf=0.77)
Experimental — accuracy not yet measuredBy comparing data from published literature with laboratory test results posted on the Reddit PCOS subreddit, one study concluded that such data were representative of PCOS research cohorts, suggesting more data collected online could be used for PCOS research.
“By comparing data from published literature with laboratory test results posted on the Reddit PCOS subreddit, one study concluded that such data were representative of PCOS research cohorts, suggesting more data collected online could be used for PCOS research [76].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [76]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredA 2023 study conducted in New Zealand used machine learning to explore the feasibility of gathering and analyzing a dataset of self-reported laboratory test results posted on the PCOS subreddit on Reddit.
“A 2023 study was conducted in New Zealand using machine learning to explore the feasibility of gathering and analyzing a dataset of self-reported laboratory test results posted on the PCOS subreddit on Reddit [76].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [76]
Nine included studies mention the use of machine learning.
“Nine included studies mention the use of machine learning [63,64,65,66,67,76,87,88,89] (Supplementary File S3).”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [76]
Most machine learning studies were published in 2023 (n = 5), while two were published in 2022, and one each in 2021 and 2020.
“Most of them were published in 2023 (n = 5) [63,64,65,76,87], while two were published in 2022 [88,89], and one each in 2021 [66] and 2020 [67].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [76]
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- Authors
- Emanuel, R. H. K., Docherty, P. D., Lunt, H., & Campbell, R. E.
- Affiliations
- University of Canterbury (NZ)
- Health New Zealand (NZ)
- University of Otago (NZ)
- Corresponding author
- Emanuel, Rebecca H K
- Venue
- JMIR Formative Research
- Publisher
- JMIR Publications Inc.
- Published
- 2023-08-25
- Volume
- 7
- Pages
- e44810
- ISSN
- 2561-326X (ISSN-L)
- PMC
- PMC10492173
- OpenAlex
- W4378233588
- Cited by
- 9
- References
- 47
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 4
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Formative Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Canterbury
- Topics
- Health Sciences › Medicine › Reproductive Medicine › Ovarian function and disorders
- Social Sciences › Health › Social Media in Health Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- polycystic ovary syndrome, online health communities, PCOS phenotypes, anti-Müllerian hormone, fasting insulin, follicle-stimulating hormone, insulin resistance, sex hormone-binding globulin, testosterone, luteinizing hormone, dehydroepiandrosterone sulfate, fasting blood glucose, PCOS diagnostic criteria, Rotterdam criteria
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