Airway Hyperresponsiveness in Elite Athletes
Bibliographic record
Abstract
It has been suggested that high-level training could contribute to the development of airway hyperresponsiveness (AHR), but the comparative effects of different sports on airway function remains to be determined. We evaluated 150 nonsmoking volunteers 18 to 55 yr of age; 100 athletes divided into four subgroups of 25 subjects each according to the predominant estimated hydrocaloric characteristic of ambient air inhaled during training: dry air (DA), cold air (CA), humid air (HA) and a mixture of dry and humid air (MA), and 50 sedentary subjects. Each subject had a respiratory questionnaire, a methacholine challenge, allergy skin-prick tests, and heart rate variability recording for evaluation of parasympathetic tone. The athletes had a 49% prevalence of AHR (PC(20) < 16 mg/ml), with a mean PC(20) of 16.9 mg/ml, compared with 28% (PC(20): 35.4) in sedentary subjects (p = 0.009). The prevalence (%) of AHR and mean PC(20) (mg/ml) varied as followed in the four subgroups of athletes: DA: 32% and 30.9; CA: 52% and 15.8; HA: 76% and 7.3; and MA: 32% and 21.5 (p = 0.002). The estimated parasympathetic tone was higher in athletes (p < 0.001), but this parameter showed only a weak correlation with PC(20) (r = -0.17, p = 0.04). This study has shown a significantly higher prevalence of AHR in athletes than in the control group because of the higher prevalence in the CA and HA groups. Parasympathetic activity may act as modulator of airway responsiveness, but the increased prevalence of AHR in our athlete population may be related to the type and possibly the content of inhaled air during training.
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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".