Diagnostic Performance of Ovarian Morphology on Ultrasonography across Anovulatory Conditions—Impact of Body Mass Index
Bibliographic record
Abstract
The study objectives were to determine whether ovarian morphology can distinguish between women with regular menstrual cycles, normo-androgenic anovulation (NA-Anov), and PCOS and whether body mass index (BMI)-specific thresholds improved diagnostic potential. Women with PCOS (biochemical and/or clinical hyperandrogenism and irregular cycles; N = 66), NA-Anov (irregular cycles without clinical and/or biochemical hyperandrogenism; N = 64), or regular cycles (controls; cycles every 21–35 days in the absence of clinical or biochemical hyperandrogenism; N = 51) were evaluated. Participants underwent a reproductive history, physical exam, transvaginal ultrasound, and a fasting blood sample. Linear regression analyses were used to assess the impact of BMI on ovarian morphology across groups. The diagnostic performance of ovarian morphology for anovulatory conditions, and by BMI (lean: <25 kg/m2; overweight: ≥25 kg/m2), was tested using Receiver Operating Characteristic (ROC) curves. Follicle number per ovary (FNPO) and ovarian volume (OV), but not follicle number per cross-section (FNPS), increased across controls, NA-Anov, and PCOS. Overall, FNPO had the best diagnostic performance for PCOS versus controls (AUCROC = 0.815) and NA-Anov and controls (AUCROC = 0.704), and OV to differentiate between PCOS and NA-Anov (AUCROC = 0.698). In lean women, FNPO best differentiated between PCOS and controls (AUCROC = 0.843) and PCOS versus NA-Anov (AUCROC = 0.710). FNPS better distinguished between NA-Anov and controls (AUCROC = 0.687), although diagnostic performance was lower than when thresholds were generated using all participants. In women with overweight and obesity, OV persisted as the best diagnostic feature across all analyses (PCOS versus control, AUCROC = 0.885; PCOS versus NA-Anov, AUCROC = 0.673; NA-Anov versus controls, AUCROC = 0.754). Ovarian morphology holds diagnostic potential to distinguish between NA-Anov and PCOS, with marginal differences in diagnostic potential when participants were stratified by BMI suggesting that follicle number may provide better diagnostic performance in lean women and ovarian size in those with overweight.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".