Fine Particulate Air Pollution (PM2.5) and the Risk of Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Short-term changes in levels of fine ambient particulate matter (PM2.5) may increase the risk of acute ischemic stroke; however, results from prior studies have been inconsistent. We examined this hypothesis using data from a multicenter prospective stroke registry. METHODS: We analyzed data from 9202 patients hospitalized with acute ischemic stroke, having a documented date and time of stroke onset, and residing within 50 km of a PM2.5 monitor in 8 cities in Ontario, Canada. We evaluated the risk of ischemic stroke onset associated with PM2.5 in each city using a time-stratified case-crossover design, matching on day of week and time of day. We then combined these city-specific estimates using random-effects meta-analysis techniques. We examined whether the effects of PM2.5 differed across strata defined by patient characteristics and ischemic stroke etiology. RESULTS: Overall, PM2.5 was associated with a -0.7% change in ischemic stroke risk per 10-μg/m increase in PM2.5 (95% confidence interval = -6.3% to 5.1%). These overall negative results were robust to a number of sensitivity analyses. Among patients with diabetes mellitus, PM2.5 was associated with an 11% increase in ischemic stroke risk (1% to 22%). The association between PM2.5 and ischemic stroke risk varied according to stroke etiology, with the strongest associations observed for strokes due to large-artery atherosclerosis and small-vessel occlusion. CONCLUSIONS: These results do not support the hypothesis that short-term increases in PM2.5 levels are associated with ischemic stroke risk overall. However, specific patient subgroups may be at increased risk of particulate-related ischemic strokes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".