Urban air pollution and emergency department visits for influenza
Bibliographic record
Abstract
Introduction. There is a large body of research which suggests that air pollutants might affect infectious diseases, their transmission, severity, and a length of recovery. Aim. The aim of this study is to examine the relationships between ambient air pollution and emergency department (ED) visits for influenza and viral pneumonia in Toronto, Canada. Material and Methods. The National Ambulatory Care Reporting System database was used to drawn ED visits (4 282 days). Five ambient air pollutants: carbon monoxide, nitrogen dioxide, sulphur dioxide, ozone (CO, NO2, SO2, O3, O3H8 – ozone as a maximum eight hour average, respectively), and fine particulate matter (PM2.5) were examined. In addition, the Air Quality Health Index (AQHI; combines NO2, O3, and PM2.5) was tested. Conditional Poisson models were constructed using daily counts of ED visits. Temperature and relative humidity in the models were represented by natural splines. Air pollutants and weather factors were lagged by 0 to 14 days. The analysis was done by strata of age group, sex, and two seasons. Results. In the period of the study, 26,200 ED visits were identified; 13,963 for females and 12,237 for males. For each air pollutant, 270 models were generated (18 strata × 15 lags). Ambient air pollution concentrations
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.006 | 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".