Pregnancy-related cardiovascular risk indicators: Primary care approach to postpartum management and prevention of future disease.
Bibliographic record
Abstract
OBJECTIVE: To define pregnancy-related cardiovascular risk indicators and their association with developing future cardiovascular disease (CVD), and to provide guidance on how primary care providers can help lower future CVD risk through early identification and intervention. SOURCES OF INFORMATION: Primary research sources, systematic reviews and meta-analyses, and clinical review articles. MAIN MESSAGE: Cardiovascular disease is the leading cause of death in women. As underlying CVD risk factors are often present for years before the onset of CVD, it is important to use innovative ways to identify women who should undergo CVD risk screening at a younger age. Pregnancy and the postpartum period afford that opportunity, given that the development of certain pregnancy complications (hypertensive disorders of pregnancy, gestational diabetes mellitus, idiopathic preterm birth, delivery of a baby with intrauterine growth restriction, or placental abruption) can reliably identify women with underlying, often unrecognized, CVD risk factors. CONCLUSION: Women with 1 or more of these pregnancy complications should be identified at the time of delivery and have formalized postpartum follow-up, including a thorough history, a physical examination, biochemical screening, counseling around lifestyle modification, and possible therapeutic intervention. The link between pregnancy complications and future CVD affords the earliest opportunity for CVD risk assessment for health preservation and disease prevention.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".