Nutrition Interventions for Lowering Cardiovascular Risk After Hypertensive Disorders of Pregnancy: A Systematic Review
Bibliographic record
Abstract
Background: Hypertension is one of the most common medical problems during pregnancy. Hypertensive disorders of pregnancy (HDP) increase the risk of premature cardiovascular disease (CVD) 2- to 4-fold within 10 years after delivery. Early health behaviour modifications may prevent or manage several cardiovascular risk factors. Importantly, compared with women without HDP, fewer women with HDP achieve national dietary guidelines to prevent CVD. This highlights an opportunity for programs tailored for women post-HDP to support their nutritional behaviours as a key component of postpartum CVD preventive care. This systematic review investigated the impacts of nutrition modifications on lowering measures of CVD risk after HDP. Methods: Four electronic databases (MEDLINE, EMBASE, CINAHL, Cochrane Library) were searched in October 2022 with a search strategy focused on nutrition programs/interventions and women post-HDP. Additional inclusion criteria were original research and reported outcome of CVD risk or cardiovascular risk factors. Results: Six studies were included: 4 experimental trials and 2 prospective cohort studies. Of the nutrition interventions, 4 were embedded within comprehensive health behaviour intervention programs. Outcome measures varied, but all studies reported blood pressure. A narrative synthesis found that the range of changes in blood pressure varied from no change to clinically meaningful change. Conclusions: This review found statistically nonsignificant yet clinically important improvements in measures of cardiovascular risk across a range of nutritional interventions in women after HDP. Further high-quality evidence is needed to inform the design and implementation of nutritional preventive cardiovascular care targeting this high CVD-risk population.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".