Vascular protection in peripheral artery disease: systematic review and modelling study
Bibliographic record
Abstract
AIMS: To ascertain the effectiveness of medical therapy for reducing risk in peripheral artery disease (PAD) and to model the potential impact of combining multiple efficacious approaches. METHODS AND RESULTS: 17 electronic databases, reference lists of primary studies, clinical practice guidelines, review articles, trial registries and conference proceedings from cardiology, vascular surgery and atherosclerosis meetings were screened. Eligible studies were randomized trials or meta-analyses of randomized trials of medical therapy for PAD which reported major cardiovascular events (myocardial infarction, stroke and cardiovascular death). Baseline event rates for modelling analyses were derived from published natural history cohorts. Overall, three strategies had persuasive evidence for reducing risk in PAD: antiplatelet agents (pooled RRR 26%, 95% CI 10 to 42), statins (pooled RRR 26%, 95% CI 18 to 33) and angiotensin-converting enzyme inhibitors (individual trial RRR 25%, 95% CI 8 to 39). The estimated cumulative relative risk reduction for all three strategies was 59% (CI 32 to 76). Given a 5-year major cardiovascular event rate of 25%, the corresponding absolute risk reduction and number needed to treat to prevent one event were 15% (CI 8 to 19) and 7 (CI 5 to 12), respectively. Population level analyses suggest that increased uptake of these modalities could prevent more than 200 000 events in patients with PAD each year. CONCLUSION: The use of multiple efficacious strategies has the potential to substantially reduce the cardiovascular burden of PAD. However, these data should be regarded as hypothetical, since they are based on mathematical modelling rather than factorial randomized trials.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".