Urban air pollution and emergency department visits for influenza
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it