Prediabetes and perinatal mortality.
Bibliographic record
Abstract
OBJECTIVE: The association between gestational diabetes mellitus (GDM) and perinatal outcome is largely based on case series and retrospective studies that found an increased risk of perinatal mortality and stillbirth as the onset of diabetes approached. Our objective was to assess the relationship between latency to diabetes and perinatal outcome of prediabetic pregnancies in a contemporary population of women with adult-onset diabetes. RESEARCH DESIGN AND METHODS: A population of 403 diabetic women from two recruitment sites completed a pretested questionnaire. RESULTS: Details of 1,181 pregnancy outcomes were obtained. This comprised 1,024 live births, 22 stillbirths, and 8 early neonatal deaths. Crude analysis suggested a relationship between time to diabetes (latency) < or =20 years and both perinatal death and stillbirth: odds ratio (95% CI), 2.41 (1.17-4.95) and 2.15 (0.93-4.98). Generalized additive modeling revealed a nonlinear relationship between the variables time to diabetes, and maternal age and perinatal outcome. Final logistic regression analysis was then performed for the outcomes perinatal death and stillbirth, with maternal age as a second-degree polynomial, year of birth as a continuous variable, and time to diabetes dichotomized < or =20 years to diagnosis and >20 years. This final analysis documented a significant association between time to diabetes < or =20 years and both perinatal death (4.06 [1.79-9.36]) and stillbirth (3.35 [1.25-9.05]). CONCLUSIONS: There appeared to be an increased risk of perinatal death and stillbirth in pregnancies occurring in the last 20 years before the diagnosis of 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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.001 |
| 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".