Long-term exposure to traffic-related air pollution and stroke: A systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Stroke remains the second cause of death worldwide. The mechanisms underlying the adverse association of exposure to traffic-related air pollution (TRAP) with overall cardiovascular disease may also apply to stroke. Our objective was to systematically evaluate the epidemiological evidence regarding the associations of long-term exposure to TRAP with stroke. METHODS: PubMed and LUDOK electronic databases were searched systematically for observational epidemiological studies from 1980 through 2019 on long-term exposure to TRAP and stroke with an update in January 2022. TRAP was defined according to a comprehensive protocol based on pollutant and exposure assessment methods or proximity metrics. Study selection, data extraction, risk of bias (RoB) and confidence assessments were conducted according to standardized protocols. We performed meta-analyses using random effects models; sensitivity analyses were assessed by geographic area, RoB, fatality, traffic specificity and new studies. RESULTS: with little to moderate heterogeneity based on 6, 5, 4, 7 and 8 studies, respectively. The confidence assessments regarding the quality of the body of evidence and separately regarding the presence of an association of TRAP with stroke considering all available evidence were rated low and moderate, respectively. CONCLUSION: The available literature provides low to moderate evidence for an association of TRAP with stroke.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it