Seasonal Fluctuations in Airway Responsiveness in Elite Endurance Athletes
Bibliographic record
Abstract
BACKGROUND: It has been suggested that exposure to winter training conditions (irritants in indoor facilities and/or cold, dry air in the outdoors) can increase airway responsiveness in elite endurance athletes. OBJECTIVES: It has yet to be elucidated whether elite endurance athletes experience seasonal fluctuations in their airway responsiveness. METHODS: Eighteen members of a varsity cross-country running team underwent screening procedures and five members were enrolled in the study. Each athlete completed a respiratory and training questionnaire, and underwent allergy skin prick testing. Airway responsiveness was evaluated using a methacholine challenge on four occasions. RESULTS: The participants demonstrated a significant (more than twofold) increase in airway responsiveness (P=0.0496) during the first winter evaluation compared with the autumn baseline. The second winter evaluation still showed an increase but it was not statistically significant. Airway responsiveness had returned to baseline (autumn) values at spring testing. CONCLUSION: Elite endurance athletes experience seasonal fluctuations in airway responsiveness. The specific stimuli that cause this are unknown, but it is speculated to be due to exposure to cold, dry air and/or inhaled irritants that may be present in indoor training facilities.
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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 | 0.000 |
| 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".