OP07.06: Understanding dysmenorrhea in women with polycystic ovary morphology: a retrospective study
Bibliographic record
Abstract
Polycystic ovary morphology (PCOM) is a diagnostic criterion for polycystic ovary syndrome (PCOS) that also exists as a separate entity. We aim to investigate prevalence of dysmenorrhea in the PCOM population, determine overall PCOM prevalence, and identify potential risk factors for dysmenorrhea. We retrospectively analysed data from patients aged 20 to 45 who underwent transvaginal scans between February and June 2023 at an ultrasound clinic specialised in gynecologic diseases. PCOM was diagnosed according to the latest PCOM ultrasound criteria. Patients without dysmenorrhea information were excluded. Prevalence of dysmenorrhea in PCOM patients was calculated, and baseline characteristics were compared between groups with mild (VAS < 6) and severe (VAS > 6) dysmenorrhea. Binary logistic regression analysis was run to identify potential risk factors for dysmenorrhea in PCOM patients. PCOM was found in 243/1321(18%) patients. After exclusion, 209 patients were analysed, with 39% experiencing mild and 61% severe dysmenorrhea. Mean age differed significantly between groups (p = 0.03). Severe dysmenorrhea was associated with heavy flow (p < 0.001) and dyspareunia (p = 0.01), while previous pregnancy correlated with milder dysmenorrhea (p = 0.007). Infertility was associated with more severe dysmenorrhea (p = 0.04). Adenomyosis was more common in PCOM patients with severe dysmenorrhea (p = 0.05). Heavy flow was identified as risk factor for dysmenorrhea (OR, 16.32; p < 0.001), while gravidity was protective (OR, 0.38; p = 0.016). Patients with PCOM referred for advanced endometriosis scan were more likely to have severe dysmenorrhea (OR, 4.34; P < 0.021). This study highlights severity of dysmenorrhea in individuals with PCOM and provides for the first time overall prevalence of PCOM, as a separate entity from PCOS. Further research is needed to understand the underlying pathophysiological mechanism of pain development in PCOM population, and exploring associations with other gynecological conditions is crucial for tailored treatment plan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".