Utility of Polycystic Ovarian Morphology on Ultrasonography Across Menstrual Cycle Phases to Aid in the Diagnosis of Polycystic Ovary Syndrome ( <scp>PCOS</scp> )
Bibliographic record
Abstract
Objectives Determine the diagnostic accuracy of ovarian morphology on ultrasonography for polycystic ovary syndrome (PCOS) across the menstrual cycle. Methods Data from 25 women with PCOS (oligo‐anovulation + androgen excess) and 40 age‐ and BMI‐matched controls (regular cycles + normal androgens) with consecutive ultrasound scans over ≥2 menstrual cycle phases were included in this retrospective analysis. Phases of interest included: early follicular (1–6 days following menses onset), late follicular (1–7 days prior to ovulation in the presence of a dominant follicle), early luteal (1–7 days following ovulation), and late luteal (1–7 days prior to the onset of menses). Diagnostic accuracy (area under the ROC curve [AUC], sensitivity [Se], specificity [Sp]) of mean, maximum, and contralateral (ovary without dominant follicle or corpus luteum) sonographic measures for follicle number per ovary (FNPO), follicle number per single section (FNPS), and ovarian volume (OV) at each phase were determined. DeLong tests determined differences in diagnostic accuracy across phases. Results FNPO, FNPS, and OV all had significant diagnostic accuracy for PCOS across menstrual cycle phases. OV mean had the highest diagnostic accuracy for PCOS in the early follicular phase (AUC = 0.87, Se = 65%, Sp = 95%), whereas OV contralateral had the highest accuracy in both the early (AUC = 0.81, Se = 62%, Sp = 92%) and late luteal phases (AUC = 0.93, Se = 100%, Sp = 70%). OV contralateral outperformed all other measures in the late luteal phase ( P < .05). Conclusion Ultrasonographic evaluations for PCOS may be performed across the menstrual cycle. The presence of a dominant follicle in the late follicular phase did not impact the performance of ovarian markers for PCOS status. By contrast, polycystic ovarian morphology is best defined in the luteal phase by assessments of FNPO and OV in the contralateral ovary.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".