Relationship between the follicular distribution pattern of polycystic ovaries and the degree of menstrual disturbance and serum sex steroid levels
Bibliographic record
Abstract
Objective: This study aimed to examine the associations between follicular distribution pattern (FDP) in polycystic ovaries and menstrual disturbances in women with infertility. Materials and Methods: A retrospective review of patients was performed (n=73). Ultrasound images from cycle day 2-5 of a spontaneous or progestin induced menstrual cycle were reviewed. Ovaries were classified as polycystic ovarian morphology (PCOM) if they contained ≥12-follicles measuring 2-9 mm in diameter. Images of PCOM ovaries were classified as having a peripheral cystic pattern (PCP) with follicles arranged at the periphery of the ovary, or general cystic pattern (GCP) if follicles were dispersed heterogeneously throughout the ovarian stroma. Menstrual disturbance was assessed by questionnaire, and oligomenorrhea was defined as cycles >35 days in length. Results: PCP was more strongly associated with menstrual irregularity that GCP. 94% of subjects with bilateral PCP-experienced oligomenorrhea compared with 65% of women with a unilateral PCP ovary [odds ratio (OR) 9; p<0.05]. 29% of women with bilateral GCP ovaries experienced menstrual disturbances, less than bilateral PCP (OR 36; p=0.002), but similar to unilateral PCP (OR 3; p=0.07). Serum testosterone and luteinizing hormone (LH) levels were significantly correlated with the ovarian FDP. Conclusion: There is a relationship between menstrual irregularity or certain types of serum steroids and ovarian morphology. It remains unknown if morphology, testosterone or LH causes the menstrual disturbance or if they are co-initiated by an intervening factor.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".