Acute myocardial infarction triggered by physical exertion: a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS: While regular physical activity has clear benefits to cardiovascular health, physical exertion can trigger acute myocardial infarction (AMI). We aimed to estimate how many AMIs may be attributed to bouts of physical exertion and to explore possible modifiers of this association. METHODS AND RESULTS: MEDLINE, ISI Web of Science, and Scopus databases were searched for case-crossover studies reporting the relative risk (RR) of exertion-related AMI and exposure prevalence in the control periods. We used the random-effects model to pool the RR estimates and the mixed-effects model and random-effects meta-regression for subgroup analyses and estimated the population attributable fraction (PAF) at the population level and in different subgroups. The study met the Preferred Reporting Items for Systematic Reviews and Meta-Analyses requirements. Twelve studies including 19 891 AMI patients met the criteria for inclusion. There was a strong overall association between episodic physical exertion and AMI [RR = 3.46; 95% confidence interval (CI), 3.16-3.78]. The total PAF was 10.6% (95% CI, 9.44-11.83). For each additional year of age, the RR of exertion-related AMI increased by ∼3%, but the PAF decreased by 2%. For each additional time of habitual activity per week, the RR of exertion-related AMI decreased by ∼43%. The impact was greater among those engaged in physical exertion one to three times a week (≥20% of cases) and among those who did not take compared with those who took β-blockers (P = 0.049). CONCLUSION: Every tenth AMI may be assigned to physical exertion. The impact was more pronounced among younger patients, those exposed to exertion one to three times a week, and those not taking β-blockers.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".