Determinants of Small for Gestational Age in Women With Type 2 Diabetes in Pregnancy: Who Should Receive Metformin?
Bibliographic record
Abstract
OBJECTIVE: In the MiTy (Metformin in Women With Type 2 Diabetes in Pregnancy) randomized trial of metformin versus placebo added to insulin, we found numerous benefits with metformin but identified an increased proportion of infants who were small for gestational age (SGA). We aimed to determine the predictors of SGA in order to individualize care. RESEARCH DESIGN AND METHODS: Using logistic regression, we assessed baseline maternal characteristics as predictors of SGA. We compared maternal/neonatal outcomes in SGA metformin and placebo groups using the t, χ2, or Fisher exact test, as appropriate. RESULTS: Among the 502 mothers, 460 infants were eligible for this study. There were 30 infants with SGA in the metformin group (12.9%) and 15 in the placebo group (6.6%) (P = 0.026). Among SGA infants, those in the metformin group were delivered significantly later than those in the placebo group (37.2 vs. 35.3 weeks; P = 0.038). In adjusted analyses, presence of a comorbidity (chronic hypertension and/or nephropathy) (odds ratio [OR] 3.05; 95% CI 1.58-5.81) and metformin use (OR 2.26; 95% CI 1.19-4.74) were predictive of SGA. The absolute risk of SGA was much higher in women receiving metformin with comorbidity compared with women receiving metformin without comorbidity (25.0% vs. 9.8%). CONCLUSIONS: In this study, we observed a high percentage of SGA births among women with type 2 diabetes and chronic hypertension and/or nephropathy who were treated with metformin. Therefore, with the aim of reducing SGA, it is reasonable to be cautious in our use of metformin in those with type 2 diabetes and chronic hypertension or nephropathy in pregnancy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".