Premature Cardiovascular Death and its Modifiable Risk Factors: Protocol of a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
ABSTRACT Introduction The burden of cardiovascular disease (CVDs), in the number of premature deaths, continues to increase globally and alarmingly, especially in almost all countries outside high-income countries. An accurate estimate of premature CVD death is of crucial importance for planning, implementing, and evaluating cardiovascular prevention and care interventions. However, existing literature reported the evidence on premature CVD death from selected regions, and there has been less evidence of systematic review with meta-analysis estimating the global premature death. This paper reports the protocol for a systematic review and meta-analysis to derive solid and updated estimates on global and setting-specific premature CVD death prevalence and its associated risk factors. Methods and analysis PUBMED, EMBASE, Web of Science, CINAHL, and Cochrane Central Register of Controlled Trials (CENTRAL) will be used as the literature database. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies that report data on premature mortality (death before the age of 70 years) related to CVD. Study selection and reporting will follow the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guideline. Pooled estimates of premature CVD mortality (based on standardised mortality ratio and years of life lost) and the effect size of modifiable risk factors will be computed applying random-effects meta-analysis. Heterogeneity among selected studies will be assessed using the I 2 statistic and explored through meta-regression and subgroup analyses. Depending on data availability, we propose to conduct subgroup analyses by geographical area, CVD events, and socio-demographic variables of interest study. The risk of bias for the studies included in the systematic review or meta-analysis will be assessed by the Newcastle–Ottawa Quality Assessment Scale. Ethics and dissemination Ethics approval is not required as the data used in this systematic review will be extracted from published studies. The systematic review will focus on the premature CVD mortality rate and its associated factors. The findings of the final report will be disseminated to the scientific community through publication in a peer-reviewed journal and presentation at conferences. PROSPERO registration number CRD42021288415
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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 | 0.114 | 0.155 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.008 |
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".