Immediate risk of myocardial infarction following physical exertion, tea, and coffee: A case-crossover study in Thailand
Bibliographic record
Abstract
BACKGROUND: Physical exertion and caffeine consumption are associated with acute myocardial infarction (MI). However, physical exertion and caffeine consumption have not been examined as immediate triggers of MI in low and middle-income countries. OBJECTIVE: Using a self-matched case-crossover design, we examined the acute risk of MI in the hour following episodes of physical exertion, caffeinated coffee, and tea consumption among MI survivors in Thailand. METHODS: A total of 506 Thai participants (women = 191, men = 315) were interviewed between 2014 and 2017 after sustaining an acute MI. We compared each subject's exposure to physical exertion and consumption of caffeine- containing beverages in the hour preceding the onset of MI with the subject's expected usual frequency in the prior year to calculate relative risks (RRs) and 95% confidence intervals (95%CIs). RESULTS: Of the 506 participants, 47 (9.3%) engaged in moderate or heavy physical exertion, 6 (1.2%) consumed tea, and 21 (4.2%) consumed coffee within the hour before MI. The relative risk of MI after moderate or heavy physical exertion was 3.0 (95% CI 2.2-4.2) compared to periods of no exertion, with a higher risk among more sedentary participants compared to active participants. Compared to times with no caffeinated beverage consumption, there was a higher risk of MI in the hour following consumption of caffeinated tea (RR = 3.7; 95%CI: 1.5-9.3) and coffee (RR = 2.3; 95%CI: 1.4-3.6). CONCLUSION: Physical exertion, coffee and tea consumption were associated with a higher risk of MI in the subsequent hour compared to times when the participants were sedentary or did not consume caffeinated beverages. Our study identifies high-risk populations for targeted screening and intervention to prevent acute MI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".