The air quality health index and emergency department visits for injury
Bibliographic record
Abstract
Abstract Introduction. The purpose of this study was to investigate the associations of ambient air quality with emergency department (ED) visits for injury. Aim. To explore correlations between ED visits for injury and ambient air pollution. Materials and methods. Considered health outcomes are ED visits for injury (ICD-9 codes: 800-999) in Edmonton, Canada, for the period from April 1998 to March 2002 (1,444 days). Air pollution concentration in the ambient air is represented as a daily maximum of the Air Quality Health Index (AQHI). The AQHI value encapsulates levels of three urban ambient air pollutants (ozone, nitrogen dioxide and fine particulate matter), weighted by constant risk coefficients. A time-stratified casecrossover design, using conditional logistic regression and conditional Poisson regression, was realized to assess the associations. The risk, reported as odds ratio and relative risk, was estimated using log-linear models and parametric non-linear concentrationresponse functions. Results. The strongest effects were observed for young male patients in the cold season (October-March). Lagged exposures were found to have positive statistically significant associations. Discussion. The study results indicate that air quality was associated with increased risk of daily ED visits for injury. This study determined concentration-response functions which allow one to assess the effects for various levels of the AQHI.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".