Diabetes and Perinatal Mortality in Twin Pregnancies
Bibliographic record
Abstract
BACKGROUND: Diabetes in pregnancy has been associated with a paradoxically reduced risk of neonatal death in twin pregnancies. Risk "shift" may be a concern in that the reduction in neonatal deaths may be due to an increase in fetal deaths (stillbirths). This study aimed to clarify the impact of diabetes on the risk of perinatal death (neonatal death plus stillbirth) in twin pregnancies. METHODS: This was a retrospective cohort study of twin births using the largest available dataset on twin births (the U.S. matched multiple birth data 1995-2000; 19,676 neonates from diabetic pregnancies, 541,481 from non-diabetic pregnancies). Cox proportional hazard models were applied to estimate the adjusted hazard ratios (aHR) of perinatal death accounting for twin cluster-level dependence. RESULTS: Comparing diabetic versus non-diabetic twin pregnancies, overall perinatal mortality rate was counterintuitively lower [2.1% versus 3.3%, aHR 0.70 (95% confidence intervals 0.63-0.78)]. Individually, both stillbirth and neonatal mortality rates were lower in diabetic pregnancies, but we identified significant differences by gestational age and birth weight. Diabetes was associated with a survival benefit in pregnancies completed before 32 weeks [aHR 0.55 (0.48-0.63)] or with birth weight <1500 g [aHR 0.61 (0.53-0.69)]. In contrast, diabetes was associated with an elevated risk of perinatal death in pregnancies delivered between 32 and 36 weeks [aHR 1.38 (1.10-1.72)] or with birth weight >=2500 g [aHR 2.20 (1.55-3.13)]. CONCLUSIONS: Diabetes in pregnancy appears to be "protective" against perinatal death in twin pregnancies ending in very preterm or very low birth weight births. Prospective studies are required to clarify whether these patterns of risk are real, or they are artifacts of unmeasured confounders. Additional data correlating these outcomes with the types of diabetes in pregnancy are also needed to distinguish the effects of pre-gestational vs. gestational diabetes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".