The 2023 Canadian Wildfires and Risk of Hospitalization and Mortality Among Hemodialysis Patients in the United States
Bibliographic record
Abstract
Introduction: Smoke plumes from the 2023 Canadian wildfires severely impacted air quality across the Eastern and Midwestern USA. However, a comprehensive health impact assessment is lacking in this large region. We investigated the association between wildfire-related air pollutants and the risk of mortality and hospitalization among hemodialysis patients in 22 heavily impacted states in the Eastern and Midwestern USA. Methods: ) concentrations were assessed using satellite-derived smoke polygons and ground-based monitors. Daily number of all-cause deaths, all-cause hospitalizations, respiratory disease hospitalizations, and cardiovascular disease hospitalizations were counted for each hemodialysis clinic. Results: was associated with a 139% increase in same day all-cause mortality (RR: 2.39; 95% CI: 1.79-3.18), and a 33% increase in all-cause hospitalization (RR:1.33; 95% CI: 1.10-1.62). Conclusion: Our findings suggest that air pollution from the 2023 Canadian wildfires resulted in increased risk of mortality and hospitalization among hemodialysis patients in Eastern and Midwestern USA.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".