Within-city spatiotemporal variations in outdoor ultrafine particles and emergency room visits for cardiovascular outcomes
Bibliographic record
Abstract
Background: Few epidemiological studies have examined the acute cardiovascular health impacts of within-city spatiotemporal variations in outdoor ultrafine particles (UFPs). Methods: We conducted a time-stratified case-crossover study of within-city spatiotemporal variations in outdoor UFPs and emergency room visits for cardiovascular outcomes in Toronto, Canada, between 2019 and 2020. Outdoor UFP data (lag-0, 3-day mean, and 7-day mean) were assigned to residential locations using exposure models trained using data collected over a 1 year period (2020–2021). Conditional logistic regression models were used to estimate odds ratios (ORs) (95% confidence intervals [CIs]) for outdoor UFP number concentrations (per 10,000/cm3) and mean UFP size (per 5 nm), adjusting for potential time-varying confounders. Effect modification by temperature was also examined. Results: In total, our analysis included 89,694 cases of acute cardiovascular events. Seven-day mean outdoor UFP number concentrations and mean UFP size were most strongly associated with cardiovascular outcomes. Specifically, 7-day mean outdoor UFP number concentrations were associated with increased risks of all cardiovascular events (OR = 1.058, 95% CI = 1.007, 1.111) and ischemic heart disease events (OR = 1.140, 95% CI = 1.020, 1.274). Likewise, 7-day mean outdoor UFP size was associated with increased risks of all cardiovascular events (OR = 1.026, 95% CI = 1.013, 1.039) and ischemic heart disease events (OR = 1.059, 95% CI = 1.029, 1.090). Submultiplicative and subadditive interactions were observed between temperature and both outdoor UFP number concentrations and mean UFP size. Conclusions: Within-city spatiotemporal variations in outdoor UFPs are associated with an increased risk of cardiovascular events independent of other common outdoor air pollutants.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".