Air pollution and emergency department visits for urticaria
Bibliographic record
Abstract
Abstract Introduction. This study investigates associations between air pollution and emergency department (ED) visits for urticaria in Toronto, Canada. Aim. To verify the hypothesis that urticaria are related to air pollution. Material and methods. The National Ambulatory Care Reporting System database is used to draw the daily ED visits. The L50 section of the International Classification of Disease 10th Revision is applied to extract ED visits whose primary causes was urticaria-related skin condition. Statistical models (condition Poisson regression) using daily counts of ED visits are constructed for urticaria (health response) with ambient air pollution concentrations and weather factors as independent variable. Two air quality health indexes and six ambient air pollutants: fine particulate matter PM2.5, O3, CO, NO2, SO2, and maximum 8-hour average ozone are considered as an exposure. Results. A total of 176 statistically significant (P-Value <0.05) positive correlations were identified over the 15 day lag period (0-14 days). For daily average of ambient ozone, 74 positive correlations were observed with the following relative risks (RR) for a one interquartile range (IQR=12.8 ppb) increase: RR=1.361 (95% confidence interval: 1.302, 1.404), 1.359 (1.299, 1.401), 1.351 (1.281, 1.404) in the warm season (April-September), lag 0, and RR=1.019 (1.013, 1.025), 1.023 (1.016, 1.030), 1.014 (1.007, 1.021), lag 1, in the cold period (October-March), for all, females, and males, respectively. 10, 45 and 45 positive correlations were also obtained for sulfur dioxide, fine particulate matter, and daily maximum 8-hour average ozone concentrations, respectively. Conclusions. The results indicate that urban ambient air pollution could influence the numbers of ED visits for urticaria. Ambient ozone was determined as the main environmental factor contributing to these associations.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".