Short‐ and long‐term outcomes of metformin compared with insulin alone in pregnancy: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: To assess the short- and long-term maternal and fetal impact of metformin in pregnancy compared with insulin. METHODS: We performed a comprehensive literature search of MEDLINE, EMBASE, BIOSIS, Cochrane Database of Systematic Reviews and ClinicalTrials.gov. Eligible studies were randomized control trials (RCTs) or follow-up of an RCT that: (1) compared metformin with insulin in pregnancy in women with gestational diabetes mellitus or Type 2 diabetes; and (2) reported maternal or fetal outcomes of interest. Two reviewers extracted the data, evaluated study quality and calculated pooled estimates. RESULTS: Sixteen studies (n = 2165 in quantitative analysis) were included. Metformin lowered the risk of neonatal hypoglycaemia [risk ratio (RR) = 0.63; 95% confidence interval (95% CI), 0.45 to 0.87], large for gestational age babies (RR = 0.80; 95% CI, 0.64 to 0.99), pregnancy-induced hypertension (RR = 0.56; 95% CI, 0.37 to 0.85) and total maternal pregnancy weight gain [mean difference (MD) -2.07; 95% CI -2.88 to -1.27]. Metformin did not increase preterm delivery (RR = 1.18; 95% CI 0.67 to 2.07), small for gestational age babies (RR = 1.20; 95% CI, 0.67 to 2.14), perinatal mortality (RR = 0.82; 95% CI, 0.17 to 3.92) or Caesarean section (RR = 0.97; 95% CI, 0.80 to 1.19). Long-term outcome information is limited. CONCLUSIONS: Our review found that metformin had no short-term adverse effects on pregnancy, potential benefits in the neonatal period, but limited long-term follow-up information. Prior to routine use, we recommend further follow-up studies of offspring exposed to metformin in utero.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".