Acute Asthma Exacerbations and Air Pollutants in Children Living in Belfast, Northern Ireland
Bibliographic record
Abstract
The incidence of childhood asthma, a common condition, is on the rise worldwide. Despite reductions in the emission of urban smoke, traffic pollution is now a major worldwide problem. Belfast, Northern Ireland, is an old industrial city with major pollution problems. In this study, the authors investigated the rates of acute asthma admissions to Belfast's major children's emergency department. The admissions were studied, relative to day-to-day fluctuations in thoracic particulate matter, sulfur dioxide, nitrogen dioxide, nitric oxide, oxides of nitrogen, ozone, carbon monoxide, benzene, temperature, and rainfall. Daily admissions for acute asthma at the emergency department of the Royal Belfast Hospital and average daily pollution were recorded for the 3-yr period between January 1, 1993, and December 31, 1995. The authors used Poisson regression to assess independent association(s). Individually, small associations were seen for thoracic particulate matter (relative risk = 1.10), sulfur dioxide (relative risk = 1.09), nitrogen dioxide (relative risk = 1.11), nitric oxide (relative risk = 1.07), oxides of nitrogen (relative risk = 1.10), carbon monoxide (relative risk = 1.07), and benzene (1.14); no associations were noted between meteorological factors (temperature and rainfall) or ozone and asthma emergency-department admissions. The authors adjusted for the aforementioned parameters, and benzene level was the only variable associated independently with asthma emergency-department admissions in children. Benzene may be a more reliable method of measuring exposure to vehicle exhaust emissions than measurements of other pollutants.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".