Tree Pollen and Hospitalization for Asthma in Urban Canada
Bibliographic record
Abstract
BACKGROUND: Allergy to tree pollen is common and aeroallergens have been associated with severe asthma exacerbations in the community setting. To determine the impact of different trees on asthma, we tested the association between daily hospitalizations for asthma and daily concentrations of different tree pollens in 10 large Canadian cities. METHODS: Daily time-series analyses were employed to remove unwanted temporal trends. For each family or genus, results were adjusted for day of the week, temperature, barometric pressure and relative humidity. Results were expressed as the percentage increase in asthma hospitalizations related to an increase in tree pollen concentration equivalent in magnitude to its interquartile range. RESULTS: For an interquartile increase in daily tree pollen concentration, percent increases in daily hospitalization for asthma were: 2.63% (95% CI 1.19-4.07) for Ulmus (elm), 2.45% (1.12-3.78) for the group containing Pinaceae (pine, fir, spruce), Tsuga (hemlock) and Larix (larch, tamarack); 2.32% (0.93-3.71) for the group containing Quercus (oak) and Castanea (chestnut), and 2.16% (0.70-3.62) for Acer (boxelder and maple). Statistically significant (p < 0.05) but small (<2%) effects were observed for Fraxinus (ash), Populus (aspen, poplar), Alnus (alder), Betula (birch) and Corylus (hazelnut). CONCLUSIONS: Several common tree pollens are an important cause of acute exacerbations of asthma severe enough to require hospitalization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".