Anger or emotional upset and heavy physical exertion as triggers of stroke: the INTERSTROKE study
Bibliographic record
Abstract
AIMS: In INTERSTROKE, we explored the association of anger or emotional upset and heavy physical exertion with acute stroke, to determine the importance of triggers in a large, international population. METHODS AND RESULTS: INTERSTROKE was a case-control study of first stroke in 32 countries. Using 13 462 cases of acute stroke we adopted a case-crossover approach to determine whether a trigger within 1 hour of symptom onset (case period), vs. the same time on the previous day (control period), was associated with acute stroke. A total of 9.2% (n = 1233) were angry or emotional upset and 5.3% (n = 708) engaged in heavy physical exertion during the case period. Anger or emotional upset in the case period was associated with increased odds of all stroke [odds ratio (OR) 1.37, 99% confidence interval (CI), 1.15-1.64], ischaemic stroke (OR 1.22, 99% CI, 1.00-1.49), and intracerebral haemorrhage (ICH) (OR 2.05, 99% CI 1.40-2.99). Heavy physical exertion in the case period was associated with increased odds of ICH (OR 1.62, 99% CI 1.03-2.55) but not with all stroke or ischaemic stroke. There was no modifying effect by region, prior cardiovascular disease, risk factors, cardiovascular medications, time, or day of symptom onset. Compared with exposure to neither trigger during the control period, the odds of stroke associated with exposure to both triggers were not additive. CONCLUSION: Acute anger or emotional upset was associated with the onset of all stroke, ischaemic stroke, and ICH, while acute heavy physical exertion was associated with ICH only.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".