Atherosclerotic plaque features and risk of cardiovascular events: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Strokes and myocardial infarctions, caused by the rupture of unstable atherosclerotic plaques in the carotid and coronary arteries, respectively, are leading causes of death and disability world-wide. Current clinical guidelines recommend surgical intervention of plaques based solely on the degree of artery stenosis. However, it is increasingly recognized that plaque morphology and composition are better predictors of clinical outcomes than artery stenosis alone. In this protocol we outlined a systematic review and meta-analysis comparing the associated risk between the prevalence/extent of various plaque features, as assessed via medical imaging of the carotid/coronary arteries, and cardiovascular/cerebrovascular events. Secondary analyses will assess the impact of combining high-risk plaque features with the degree of artery stenosis in predicting cardiovascular risk compared to stenosis alone.EVIDENCE ACQUISITION: The following electronic databases were searched: MEDLINE, Embase, The Cochrane Library, PubMed, Web of Science, grey literature, and trial registries. Search strategies were designed to identify human (≥18 years) controlled trials, cohort studies, case-control studies, and cross-sectional studies using a combination of medical subject headings and keywords. A preliminary search strategy was developed for MEDLINE (1946 - May 26, 2021).EVIDENCE SYNTHESIS: For primary outcomes, data had to include effect estimates for cardiovascular/cerebrovascular events. Secondary outcomes included effect estimates for cardiovascular and all-cause mortality, hospitalizations, and surgical intervention. Pooled analyses were conducted using the random-effects model on individual patient data and summarized study data. Publication bias were assessed by visual inspection of funnel plots and by Begg’s and Egger’s statistical tests. Between-studies heterogeneity were measured using the I2 test (P<0.10). Sources of heterogeneity were explored by sensitivity, subgroup, and meta-regression analyses.CONCLUSIONS: Findings were shared through scientific conferences and societies, social media, and consumer advocacy groups. Results were used to inform current guidelines for carotid and coronary atherosclerotic disease management. By identifying the plaque features on medical imaging that contribute to high cardiovascular (including cerebrovascular) risk, this review ultimately helped achieve early detection of unstable plaques, more accurate prediction of ischemic events, and optimal selection of patients for surgical and medical intervention.
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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 | 0.057 | 0.086 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.028 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.075 | 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".