Metformin and risk of adverse pregnancy outcomes among pregnant women with gestational diabetes in the United Kingdom: A population‐based cohort study
Bibliographic record
Abstract
AIMS: Metformin is increasingly used off-label as the treatment of gestational diabetes (GDM). Our objective was to determine if metformin versus insulin initiation is associated with the adverse pregnancy outcomes. MATERIALS AND METHODS: We conducted a retrospective cohort study using data from the Clinical Practice Research Datalink, its pregnancy register, and Hospital Episode Statistics from 1998 to 2018. We included pregnancies of women who initiated metformin or insulin between 20 weeks gestation and pregnancy end. The primary outcome was a composite outcome of large for gestational age (LGA) and macrosomia. The secondary outcomes included small for gestational age (SGA), preterm birth, caesarean delivery, and hypertensive disorders during pregnancy (HDP). Inverse probability weighted-Cox proportional hazards models were to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CI), comparing those who initiated metformin versus insulin at cohort entry, accounting for baseline covariates. RESULTS: Our cohort included pregnancies of 1297 women initiating metformin and of 895 women initiating insulin. Compared to insulin initiation, metformin initiation was associated with a decreased risk of LGA or macrosomia (HR 0.64, 95% CI 0.49, 0.78), Caesarean delivery (HR 0.83, 95% CI 0.69, 0.98), and preterm birth (HR 0.83, 95% CI 0.58, 1.08). The HRs for HDP and SGA were 0.92 (95% CI 0.57, 1.27) and 1.33 (95% CI 0.67, 2.00), respectively. CONCLUSIONS: Our study suggests that, compared to initiating insulin, initiating metformin is associated with decreased risks of adverse pregnancy outcomes among women with GDM. These findings provide important real-world evidence regarding the use of metformin for GDM.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".