Urban Air and Emergency Department Visits in Toronto, Canada
Bibliographic record
Abstract
This study examines the relationship between short-term exposure to ambient air pollution and the onset of human health conditions in Toronto, Canada. Urban air quality is influenced by various pollutants, many of which pose risks to human health. This research specifically investigates the acute effects of these pollutants in Toronto, with health outcomes measured by emergency department visits. To assess relative risks, statistical models were developed for 8 air pollutants and 18 demographic and seasonal strata (defined by sex, age, and season). Health outcomes were categorized into 12 disease groups based on the International Classification of Diseases, 10th Revision (ICD-10). The results were compiled into matrices, each containing 18 rows (strata) and 15 columns (lags) for each of the 8 pollutants and 12 health categories classified by ICD-10 codes. Estimated coefficients and their standard errors were analyzed to interpret the associations. A series of graphs were generated to visualize the effects of selected air pollutants on health. The findings highlight a significant association between ambient ozone levels and respiratory diseases (ICD-10 codes: J00–J99). Additionally, correlations were observed for certain infectious and parasitic diseases (ICD-10 codes: A00–B99). These results contribute to the growing evidence on the health impacts of urban air pollution.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".