Comparative Prevalence of Asthma in Different Groups of Athletes: A Survey
Bibliographic record
Abstract
BACKGROUND: The type of air predominantly inhaled during training seems to play an important role in the development of airway hyperresponsiveness in athletes; however, this factor has not been evaluated for asthma. OBJECTIVE AND PATIENTS: To compare the prevalence of self-reported and/or physician-diagnosed asthma among four groups of athletes categorized according to the type of air predominantly inhaled during training: cold air (n=176), dry air (n=384), humid air (n=95), and mixed dry and humid air (n=43). METHOD: Self-administrated questionnaires were used. RESULTS: One hundred seven (15.3%) of the 698 athletes reported having asthma; of these 107 athletes, 92 had physician-diagnosed asthma. No significant differences were found for the prevalence of asthma: 15.9% (cold air), 15.4% (dry air), 12.6% (humid air) and 18.6% (mixed dry and humid air), respectively (P>0.05). Furthermore, no significant differences were observed among the groups for the prevalence of confirmed atopy, cold/flu or respiratory infections (all P>0.05), except for the prevalence of hay fever, which was significantly lower among athletes of the dry air group (P=0.04). Athletes having a first-degree relative with asthma did not have a higher prevalence of asthma than those who did not (P>0.05). CONCLUSION: The prevalence of asthma was not significantly different among the four groups of athletes and it was not associated with a family history of asthma.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".