Ovulation Induction in Polycystic Ovary Syndrome
Bibliographic record
Abstract
OBJECTIVE: To review current non-pharmacologic and pharmacologic options for ovulation induction in women with polycystic ovary syndrome (PCOS). OPTIONS: This guideline reviews the evidence for the various options for ovulation induction in PCOS. OUTCOMES: Ovulation, pregnancy and live birth rates, risks, and side effects are the outcomes of interest. EVIDENCE: Published literature was retrieved through searches of Medline using appropriate controlled vocabulary and key words. Results were restricted to systematic reviews, randomized control trials/controlled clinical trials, and observational studies. Grey (unpublished) literature was identified through searching the websites of health technology assessment and of health technology assessment-related agencies, clinical practice guideline collections, clinical trial registries, and national and international medical specialty societies. VALUES: The evidence gathered was reviewed and evaluated by the Reproductive Endocrinology and Infertility Committee of the Society of Obstetricians and Gynaecologists of Canada. The quality of evidence was quantified using the Canadian Task Force on Preventive Health Care. BENEFITS, HARMS, AND COSTS: Benefits include weight reduction and improvements in ovulation, pregnancy, and live birth rates. Potential harms include medication side effects and multiple pregnancies. VALIDATION: These guidelines have been reviewed and approved by the Reproductive Endocrinology and Infertility Committee of the SOGC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".