TIMING OF AND LOCATION AT STROKE ONSET: IMPLICATIONS FOR CHARACTERIZING SHORT TERM EFFECTS FROM AMBIENT AIR POLLUTION
Bibliographic record
Abstract
Background and aims: Time-series and case-crossover studies have found that short-term increases in ambient air pollution are associated with a higher risk of stroke. These studies have typically used the day of presentation to an emergency department to infer the time of stroke. The objective of this study is to improve our understanding of potential sources of bias that could arise from the use of hospital administrative data to estimate the risk of stroke from exposure to ambient air pollution. Methods: We collected survey data from 336 stroke patients in Edmonton, Canada on the date, time, location and nature of activities at onset of stroke symptoms. The daily mean concentration of ambient NO2 and PM2.5 on the self-reported day of stroke onset was estimated from continuous fixed site monitoring stations. Results: Among the 241 patients who were able to recall when their stroke started, 72.6% experienced stroke onset the same day they presented to the emergency department; there was no systematic difference in assigned pollution levels for either NO2 or PM2.5. At the time of stroke onset, 90% were inside at the time of stroke onset. On the day of their stroke, most patients (84.5%) reported that for most of the day they were within a 15 minute drive from home. Conclusions: Our analysis suggests that day of presentation obtained from hospital administrative records can reasonably capture onset of stroke, and that any associated errors are unlikely to be important source of bias when estimating air pollution risks.
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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 | 0.032 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".