Examining the effect of salbutamol use in ozone air pollution by people with exercise‐induced bronchoconstriction
Bibliographic record
Abstract
Previous studies based on animal models have raised concerns about salbutamol use in ozone air pollution with regard to ozone related lung injury. We conducted a double-blind, randomized, placebo-controlled crossover study including 18 subjects diagnosed with EIB by a eucapnic voluntary hyperpnea (EVH) test. Participants completed 30 min of standardized moderate to vigorous exercise in four conditions: ozone plus salbutamol; room air plus salbutamol; ozone plus placebo medication; and room air plus placebo medication. Spirometry, fraction of exhaled nitric oxide, and symptoms were measured before, immediately after, 30 min after and 1 h after exercise. Measurements between the four conditions were compared using percent change from pre to post exercise. There was a statistically significant difference between the salbutamol and placebo medication groups for spirometric variables including FEV1 (Estimate = 6.3, 95% CI: 4.23-8.37, p < 0.001). No differences were observed between ozone and room air exposures. There were no significant differences in FeNO response between experimental conditions. We found that salbutamol improved pulmonary function in individuals with EIB when exercising in ozone and did not increase eosinophilic airway inflammation as indicated by FeNO. This evidence suggests that it is safe for people with EIB to continue to use salbutamol as proscribed when ozone levels are elevated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".