Risk of pre‐eclampsia in women taking metformin: a systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Aims To perform meta‐analyses of studies evaluating the risk of pre‐eclampsia in high‐risk insulin‐resistant women taking metformin prior to, or during pregnancy. Methods A search was conducted of the Medline, EMBASE, Web of Science and Scopus databases. Both randomized controlled trials and prospective observational cohort studies of metformin treatment vs. placebo/control or insulin either prior to or during pregnancy were selected. The main outcome measure was the incidence of pre‐eclampsia in each treatment group. Results Overall, in five randomized controlled trials comparing metformin treatment (n = 611) with placebo/control (n = 609), no difference in the risk of pre‐eclampsia was found [combined/pooled risk ratio (RR), 0.86 (95% CI 0.33–2.26); P = 0.76; I2 = 66%]. Meta‐analysis of four cohort studies again showed no significant effect [RR, 1.21 (95% CI 0.56–2.61); P = 0.62; I2 = 30%]. A meta‐analysis of eight randomized controlled trials comparing metformin (n = 838) with insulin (n = 836), however, showed a reduced risk of pre‐eclampsia with metformin [RR, 0.68 (95% CI 0.48–0.95); P = 0.02; I2 = 0%]. No heterogeneity was present in the metformin vs. insulin analysis of randomized controlled trials, whereas high levels of heterogeneity were present in studies comparing metformin with placebo/control. Pre‐eclampsia was a secondary outcome in most of the studies. The mean weight gain from time of enrolment to delivery was lower in the metformin group (P = 0.05, metformin vs. placebo; P = 0.004, metformin vs. insulin). Conclusions In studies randomizing pregnant women to glucose‐lowering therapy, metformin was associated with lower gestational weight gain and a lower risk of pre‐eclampsia compared with insulin.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.044 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".