P.044 Ambient air pollution and emergency department presentations for pediatric primary headache and seizure disorders in Calgary, Canada
Bibliographic record
Abstract
Background: Climate change, and fossil fuel combustion threaten the health of children globally through direct and indirect mechanisms, 1 such as the exacerbation of ambient air pollution. 1,2 Increased ambient air pollutant concentrations are associated with emergency department (ED) visits for episodic and paroxysmal neurologic conditions in adults in the Toronto region of Canada. 4,5 We hypothesize that, in Calgary, Alberta, increased ambient air pollutants will be positively associated with the daily burden of pediatric ED presentations for migraine and seizures, and that a greater effect size may be present due to increased regional variability in ambient PM2.5 concentrations. 3,4 Methods: Emergency records from the National Ambulatory Care Reporting System, comprising 17552 primary seizure and headache cases between 0-18 years of age and presenting to Calgary-region emergency departments between January 2012-December 2021, will be included. Quasi-Poisson regression modeling incorporating ambient air pollutants, seasonality and meteorological covariates will estimate relative risk and 95% confidence intervals of ED visit counts relative to increases in air pollutants. Results: Results currently pending and will be available for presentation. Conclusions: Significant results may inform further inquiry into the impact of air pollutants on children with neurological conditions and identify potential contributions of air quality to healthcare service demand in the Calgary region.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".