Pregnancy and the Postpartum Period as an Opportunity for Cardiovascular Risk Identification and Management
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of death in women. Because women generally present with more atypical symptoms of CVD than do men and because 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 us that opportunity, given that the development of certain pregnancy complications (hypertensive disorders of pregnancy, gestational diabetes, preterm birth, delivery of a neonate with fetal growth restriction, and significant placental abruption) can reliably identify women with underlying, often unrecognized, CVD risk factors. Women with one or more of these pregnancy complications should be identified at the time of delivery and referred for regular follow-up. This would ideally take the form of a multidisciplinary clinic including clinicians and allied health specialists to carry out physical and biochemical screening and counseling regarding lifestyle modification and possible therapeutic interventions. Longer-term follow-up and recommendations should be individualized based on findings and risks. There is also an opportunity for future pregnancy counseling and discussion about the importance of weight loss between pregnancies, initiation of a routine involving physical activity, use of preconception folic acid, and the potential initiation of low-dose aspirin for those women at risk for future preeclampsia and fetal growth restriction or the use of progesterone for women at risk for preterm labor. The link between pregnancy complications and future CVD affords us with 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".