Asthma, airway inflammation and epithelial damage in swimmers and cold-air athletes
Bibliographic record
Abstract
Endurance athletes show an increased prevalence of airway hyperresponsiveness. The aim of the present study was to evaluate the long-term effects of training on airway responsiveness, inflammation and epithelial damage in swimmers and cold-air athletes. In total, 64 elite athletes (32 swimmers and 32 cold-air athletes), 32 mild asthmatic subjects and 32 healthy controls underwent allergy skin prick testing, methacholine challenge and induced sputum analysis. Overall, 69% of swimmers and 28% of cold-air athletes had airway hyperresponsiveness. Sputum neutrophil count correlated with the number of training hours per week in both swimmers and cold-air athletes. Eosinophil counts were higher in swimmers than in healthy subjects, although they were lower than in asthmatic subjects, and correlated with airway hyperresponsiveness in swimmers only. The eosinophil count in cold-air athletes was similar to that in healthy subjects. Bronchial epithelial cell count was not correlated with airway hyperresponsiveness but was significantly increased in swimmers, compared with healthy and asthmatic controls. In conclusion, the present authors observed significant airway inflammation only in competitive athletes with airway hyperresponsiveness. However, the majority of elite athletes showed evidence of bronchial epithelial damage that could possibly contribute to the development of airway hyperresponsiveness.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".