Do athletes have an increased capacity to condition inspired air?
Bibliographic record
Abstract
Background: Endurance athletes have a high prevalence of airway diseases, some possibly representing adaptive mechanisms to the need of conditioning large volumes of inspired air during high ventilation in specific environments. Aim: To assess the ability of athletes to condition inspired air by measuring the difference between inhaled and exhaled air temperature (ΔT) during and after Eucapnic Voluntary Hyperpnea test (EVH). Methods: Twenty-three endurance athletes (12 with airway hyperresponsiveness (AHR) and/or exercise-induced bronchoconstriction (EIB) (A+), 11 withoutAHR and/or EIB (A-)) and 23 non-athletes (12 with AHR and/or EIB (C+) and 11 without AHR and/or EIB (C-)) were recruited. All subjects attended the laboratory on three occasions, twice for baseline characterization and once to perform a modified EVH for ΔT assessment. Inspired and expired air temperatures were measured with a high-precision probe during EVH and at regular intervals until 30 minutes after the end of the test. Results: The global ΔT during the EVH was +5.80±1.48°C and +4.73±1.51°C during the 30 minutes following the EVH. No difference was found between groups either for the ΔT or the slope of ΔT, during and after the EVH. Conclusion: This study shows no evidence of improved capacity to condition inspired air in endurance athletes, which could have suggested an increased bronchial blood flow or another adaptive mechanism. The absence of an adaptive mechanism could therefore contribute to airway damage observed in athletes in allowing colder but mainly dryer air to penetrate deeper in the lung.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".