Association between parity and gravidity & hypertension and blood pressure: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Cardiovascular disease is the leading cause of death among women worldwide, and hypertension is one of the most prevalent and modifiable risk factors. Parity and gravidity, independent of pregnancy complications, have each been associated with hypertension, although results are conflicting. Therefore, we propose to estimate the association between parity and gravidity with hypertension and blood pressure in a systematic review of the literature. METHODS AND ANALYSIS: A systematic review will be conducted to estimate the association between parity and gravidity and hypertension and blood pressure. Electronic databases (Excerpta Medica Database, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature Plus and Web of Science) will be searched from inception to January 2025. Two investigators will independently screen identified abstracts and select observational cohort studies, case-control studies and randomised controlled trials examining parity or gravidity and hypertension or blood pressure. Extracted data will include study and population characteristics, comorbidities, parity, gravidity, incidence of hypertension and changes in blood pressure, study quality and risk of bias. If there are sufficient data, they will be summarised using random effects meta-analysis to estimate the pooled risk ratio or odds ratio of hypertension. Stratified and subgroup analyses will be used to explore potential sources of heterogeneity. PROSPERO REGISTRATION NUMBER: CRD42024560535.
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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 | 0.054 | 0.075 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.026 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.005 |
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".