Cardiovascular Disease Risk Factor Interventions in Women With Prior Gestational Hypertensive Disorders or Diabetes in North America: A Rapid Review
Bibliographic record
Abstract
Women with previous hypertensive disorders of pregnancy (HDP) or gestational diabetes mellitus (GDM) have a 2- to 3-fold increased risk of cardiovascular disease (CVD). The goal of this rapid review was to summarize evidence of the effectiveness of CVD risk factor interventions for postpartum women with a history of HDP or GDM. A comprehensive search strategy was used to search articles published in 5 databases-Ovid MEDLINE, PubMed, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, and Embase). Observational and intervention studies that identified CVD prevention, screening, and/or risk factor management interventions among postpartum women with prior HDP or GDM in Canada and the US were included. The quality of observational and interventional studies, and their risk of bias, were assessed using appropriate critical appraisal checklists. Eight studies, including 4 observational cohorts, 3 randomized controlled trials, and 1 quasi-experimental study, merited inclusion for analysis. A total of 2449 participants were involved in the included studies. The most effective CVD risk factor intervention was comprised of postpartum transition and follow-up, CVD risk factor education, and advice on lifestyle changes. Most of the observational studies led to improvements in CVD risk factors, including improvements in CVD lifetime risk scores. However, none of the RCTs led to improvements in cardiometabolic risk factors. Few studies have investigated CVD risk factor interventions in the postpartum in women with previous HDP or GDM in North America. Further studies of higher quality are needed.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".