Sex differences in the efficacy of antihypertensive treatment in preventing cardiovascular outcomes and reducing blood pressure: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Hypertension is a leading cause of mortality worldwide and its prevalence is expected to rise over the next decade. Sex differences exist in the epidemiology and pathophysiology of hypertension. It is well established that antihypertensive treatment can significantly reduce the risk for stroke and other cardiovascular disease events. However, it remains unclear whether this effect is dependent on sex. In this protocol, we outlined a systematic review and meta-analysis to evaluate the effects of antihypertensive therapy in (1) reducing blood pressure and (2) preventing cardiovascular morbidity and mortality outcomes for each sex separately. Methods and analysis The following electronic databases will be searched: Medline, Embase, The Cochrane Library, PubMed, Cumulative Index of Nursing and Allied Health Literature Plus, Web of Science, grey literature (Google Scholar) and several trial registries. Search strategies will be designed to identify human adult (≥18) randomised (and non-randomised) controlled trials, prospective and retrospective cohort studies, and case–control studies concerning ‘sex-specific differences associated with the efficacy of antihypertensive treatment’. A preliminary search strategy was developed for Medline (1946—16 September 2019). Two investigators will independently review each article included in the final analysis. Primary outcomes investigated are cardiovascular morbidity and mortality and systolic and diastolic blood pressure. Pooled analyses will be conducted using the random-effects model. Publication bias will be assessed by visual inspection of funnel plots and by Begg’s and Egger’s statistical tests. Between-studies heterogeneity will be measured using the I 2 test (p<0.10). Sources of heterogeneity will be explored by sensitivity, subgroup and metaregression analyses. Ethics and dissemination This is the first meta-analysis that will comprehensively compare the efficacy of antihypertensive treatment regimens between men and women. Findings will be shared through scientific conferences and societies, social media and consumer advocacy groups. Results will be used to inform the current guidelines for management of hypertension in men and women by demonstrating the importance of implementing sex-specific recommendations. Ethical considerations are not applicable for this protocol.
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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 | 0.065 | 0.098 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.024 | 0.034 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 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".