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/cm 3 ) 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.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".