Insulin-sensitizing agents as primary therapy for patients with polycystic ovarian syndrome
Bibliographic record
Abstract
BACKGROUND: This paper is a systematic review of metformin versus clomiphene citrate (CC) in women with polycystic ovary syndrome (PCOS). METHODS: Meta-analysis Of Observational Studies in Epidemiology (MOOSE) and QUality Of Reporting Of Meta-analyses (QUOROM) guidelines were followed. A systematic computerized literature search was done of seven bibliographic databases. Inclusion criteria included cohort and randomized controlled trials (RCT) of women with PCOS and the following medications: metformin versus placebo; metformin versus CC; metformin plus CC versus placebo plus CC. Rev-man 4.1 and Metaview 4.0 were used to analyse data. Relative risk (RR) estimates were presented. A chi2-test determined the significance of the association. Heterogeneity was determined by the Cochran Q-test. RESULTS: Metformin was 50% better than placebo for ovulation induction in infertile PCOS patients [RR 1.50; 95% confidence interval (CI) 1.13, 1.99]. Metformin was also of benefit in non-infertile (i.e. patients with PCOS who were not complaining of infertility) PCOS patients for cycle regulation compared to placebo (RR 1.45; CI 1.11, 1.90). Metformin was not of confirmed benefit versus placebo for achievement of pregnancy (RR 1.07; CI 0.20, 5.74). Metformin plus CC may be 3-4-fold superior to CC alone for ovulation induction (RR 3.04; CI 1.77, 5.24) and pregnancy (RR 3.65; CI 1.11, 11.99) in women with PCOS. CONCLUSIONS: Metformin is effective for ovulation induction and cycle regulation in this group of patients. Metformin plus CC appears to be very effective for achievement of pregnancy compared to CC alone. No RCTs directly compare metformin to CC but the need for such a trial exists.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".