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Record W4413123062 · doi:10.1016/j.jeud.2025.100131

Understanding Dysmenorrhea in Individuals with Polycystic Ovary Morphology (PCOM)

2025· article· en· W4413123062 on OpenAlexaffabout
Paola Romeo, Shay Freger, K. McGowan, Mathew Leonardi

Bibliographic record

VenueJournal of Endometriosis and Uterine Disorders · 2025
Typearticle
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPolycystic ovaryOvaryMorphology (biology)MedicineGynecologyBiologyInternal medicineZoology

Abstract

fetched live from OpenAlex

• High prevalence of severe dysmenorrhea in patients with PCOM. • Heavy menstrual flow strongly predicts severe dysmenorrhea. • Gravidity appears protective against severe menstrual pain. • PCOM identified in nearly 20% of gynecologic ultrasounds. • Findings support targeted assessment for PCOM-related dysmenorrhea. Dysmenorrhea and polycystic ovarian morphology (PCOM) are prevalent gynecological conditions with significant impacts on quality of life. Although traditionally viewed as separate entities, emerging evidence suggests a potential association. This study assessed the prevalence and severity of dysmenorrhea among individuals with PCOM and identified risk factors for severe dysmenorrhea. We conducted a retrospective observational study at a gynecologic ultrasound clinic in Hamilton, Canada, including patients aged 20–45 years diagnosed with PCOM between February and June 2023. PCOM was defined by ≥20 follicles and/or ovarian volume >10 mL without a dominant follicle, cyst, or corpus luteum. Dysmenorrhea severity was self-reported via a visual analogue scale (VAS; 0–10), with VAS ≥ 6 indicating severe dysmenorrhea. Statistical analyses included univariate comparisons and logistic regression. Among 1,321 scans, 243 patients (18%) had PCOM; 208 met inclusion criteria. Severe dysmenorrhea was reported by 61% (127/208). Univariate analysis linked severe dysmenorrhea with younger age, higher weight, heavy menstrual flow, dyspareunia, and infertility history; gravidity and previous pregnancies were protective. Logistic regression identified heavy menstrual flow (OR 16.32; p < 0.001) and referral for advanced endometriosis ultrasound (OR 4.34; p = 0.021) as independent risk factors, while gravidity was protective (OR 0.38; p = 0.016). Nearly 1 in 5 patients undergoing gynecologic ultrasound presented with PCOM, and a majority reported severe dysmenorrhea. Heavy menstrual flow and gravidity emerged as key factors influencing severity. These findings underscore the importance of targeted clinical assessment and raise questions about PCOM as a contributor to menstrual pain independent of polycystic ovary syndrome.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.331
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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