Dose–response association of total and leisure-time physical activity with the risk of different subtypes of stroke: a systematic-review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This study aimed to investigate the dose-response relationship of total physical activity (TPA), leisure-time physical activity (LTPA), and occupational physical activity (OPA) with the risk of stroke. METHODS: PubMed, Cochrane Library, Embase, and Web of Science were searched to collect relevant studies on PA and stroke risk up to December 2024, and cohort research was considered to include. The Newcastle-Ottawa Scale was used to assess the quality of included studies. Egger's test and sensitive analysis were conducted. Dose-response meta-analysis and methodological quality evaluation were performed. RESULTS: A total of 23, 15, and 10 studies assessed the relationship between TPA, LTPA, and OPA with stroke. Meta-analyses results showed that higher levels of TPA, LTPA, and OPA were associated with a lower stroke risk. A nonmonotonic non-linear dose-response relationship was detected between TPA and the risk of total, hemorrhagic, and ischemic stroke. An inverse linear dose-response relationship between LTPA and the risk of total, hemorrhagic stroke, and ischemic stroke. CONCLUSION: Stroke risk is significantly reduced with increasing levels of TPA when it is lower, and has an inverse linear dose-response relationship with LTPA. A higher level of OPA is associated with lower stroke risk, but inconsistent data result in high heterogeneity.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".