Lower airway inflammatory responses to high-intensity training in athletes.
Bibliographic record
Abstract
PURPOSE: There is an increased prevalence of asthma and airway hyperresponsiveness in elite athletes, particularly in swimmers. High intensity exercise may induce airway inflammation and subsequent remodelling in these subjects. Our aim was to evaluate the effects of high-intensity training on induced-sputum cell populations in elite athletes. METHODS: Swimmers and runners with hyperresponsive airways (SH and RH), defined by a provocative concentration of methacholine inducing a 20% decrease in FEV1 (PC20) <16 mg/ml or with normoresponsive airways (PC20 > 16 mg/ml; SN, RN) to methacholine were enrolled. The mean PC20 was 2.27 mg/ml in SH (n=12), 32.2 in SN (n=10), 3.25 in RH (n=10) and 41.5 in RN (n=13). All athletes had two induced sputum analyses at one- to two-week intervals in random order: after a period of 72 hours without training, 24 hours after a training session. RESULTS: PC20 was unchanged after training. The median % neutrophils and eosinophils in groups SH, SN, RH, and RN, respectively, were 26.5-1.6, 8.6-0.3, 28.0-0.03 and 25.5-0.1 before and 45.0-0.5, 31.1-0.4, 54.0-0.6 and 48.3-0.3 after training. While the magnitude of the increase in neutrophils was similar for all groups, it reached statistical significance (pre-post-training) only in the SH group (P = 0.039). CONCLUSION: A one-hour session of high-intensity training was associated with an increase in airway neutrophils among hyperresponsive swimmer athletes, while airway responsiveness remained unchanged in all groups.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".