Considering gestational diabetes and gestational hypertension history across two pregnancies in relationship to cardiovascular disease development: A retrospective cohort study
Bibliographic record
Abstract
AIMS: Gestational diabetes (GDM) and hypertension (GHTN) occurrences signal elevated cardiovascular disease (CVD) risk. There is little study of occurrence and recurrence of these conditions in relationship to CVD. Among women with two singleton pregnancies, we aimed to quantify CVD risk in relationship to the number of GDM/GHTN occurrences. METHODS: In this Quebec-based retrospective cohort study (n = 431,980), we ascertained the number of GDM/GHTN occurrences over two pregnancies and assessed for CVD over a median of 16.4 years (cohort inception 1990-2012, outcomes 1990-2019). We defined CVD as a composite of myocardial infarction, stroke, and angina, requiring hospitalization and/or causing death. We adjusted Cox proportional hazards models for offspring size, preterm/term birth status, maternal age group, time between deliveries, ethnicity, deprivation level, and co-morbid conditions. RESULTS: Compared to absence of GDM/GHTN in either pregnancy, one GDM/GHTN occurrence increased CVD hazards by 47% (hazard ratio [HR] = 1.47, 95% confidence interval [CI] 1.35-1.61), two occurrences nearly doubled hazards (HR = 1.91, 95% CI 1.68-2.17), and three or more approximately tripled CVD hazards (HR = 2.93, 95% CI 2.20-3.90). Individual components of the composite demonstrated similar findings. CONCLUSIONS: Health care providers and mothers should consider a complete history of GDM/GHTN occurrences to ascertain the importance and urgency of preventive action.
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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) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".