Predictors of Pregnancy in Women With Polycystic Ovary Syndrome
Bibliographic record
Abstract
Two professional medical societies used expert opinion as the basis for their recommendation that current first-line treatment of polycystic ovary syndrome (PCOS)-related infertility should be ovulation induction with the selective estrogen receptor modulator, clomiphene citrate (CC), for up to 6 ovulatory cycles, with the addition of the insulin sensitizer metformin only for glucose intolerance. This prospective, multicenter, randomized clinical trial was designed to determine which baseline patient characteristics among women with PCOS were predictive of success of fertility treatment in 4 clinically relevant prognostic models of ovulation, conception, pregnancy, and live birth. Live birth was the primary study outcome. Between 2002 and 2004, ovulation was induced in 626 infertile women with PCOS by either CC plus placebo (n = 209), metformin plus placebo (n = 208), or combination CC plus metformin (n = 209). The factors that were significant for prediction of treatment success in all 4 conception models were body mass index, baseline free androgen index, proinsulin level, and duration of attempting conception. History of a prior pregnancy loss was predictive of treatment success in the models for ovulation and conception, but was not predictive of pregnancy or live birth. A low hirsutism score (<8) was not predictive of ovulation success, but was predictive of conception, pregnancy, and live birth. Age had divergent effects on ovulation and pregnancy outcomes. Age 34 or younger was a predictive factor for a successful pregnancy and live birth, whereas age greater than 34 years was predictive only of successful ovulation. Smoking history was not a significant predictor. These findings may be useful to counsel women with PCOS on their likelihood for live birth and to select infertility therapy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".