Impact of aspirin use on clinical outcomes in patients with vasospastic angina: a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives The use of aspirin to prevent cardiovascular disease in vasospastic angina (VSA) patients without significant stenosis has yet to be investigated. This study aimed to investigate the efficacy of aspirin use among VSA patients. Design Systematic review and meta-analysis. Data sources PubMed, Web of Science and Cochrane Central Register of Controlled Trials were searched for relevant information prior to October 2020. Eligibility criteria for selecting studies Aspirin use versus no aspirin use (placebo or no treatment) among VSA patients without significant stenosis. Data extraction and synthesis Two investigators extracted the study data. ORs and 95% CIs were calculated and graphed as forest plots. The Newcastle-Ottawa Quality Assessment Scale tool and Begg’s funnel plot were used to assess risk of bias. Results Four propensity-matched cohorts, one retrospective analysis and one prospective multicentre cohort, in total comprising 3661 patients (aspirin use group, n=1695; no aspirin use group, n=1966) were included in this meta-analysis. Aspirin use and the incidence of major cardiovascular adverse events with follow-up of 1–5 years were not significantly correlated (combined OR=0.90, 95% CI: 0.55 to 1.68, p=0.829, I2=82.2%; subgroup analysis: OR=1.09, 95% CI: 0.81 to 1.47, I2=0%). No significant difference was found between aspirin use and the incidence of myocardial infarction (OR=0.62, 95% CI: 0.09 to 4.36, p=0.615, I2=73.8%) or cardiac death (OR=1.73, 95% CI: 0.61 to 4.94, p=0.444, I2=0%) during follow-up. Conclusion Aspirin use may not reduce the risk of future cardiovascular events in VSA patients without significant stenosis. PROSPERO registration number CRD42020214891.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.044 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".