High-Dose Inhaled Salbutamol Does Not Improve 10-km Cycling Time Trial Performance
Bibliographic record
Abstract
BACKGROUND: β2-agonists are on the World Anti-Doping Agency's list of prohibited substances; however, athletes are allowed to treat symptoms of exercise-induced bronchoconstriction with a maximal daily dose of 1600 μg of salbutamol when taken by inhalation. PURPOSE: This study aimed to investigate whether 1600 μg of salbutamol leads to enhanced time trial performance in trained, competitive male cyclists with and without exercise-induced bronchoconstriction on the basis of inhaled dose per kilogram of body weight. METHODS: In a randomized crossover design, 20 trained male cyclists (eight with positive eucapnic voluntary hyperpnea challenge (EVH+) and 12 with negative EVH challenge (EVH-) performed two simulated 10-km time trials on a cycle ergometer 30 min after the inhalation of either 1600 μg of salbutamol or placebo. Lung function, assessed by forced expiratory volume in 1 s (FEV1), was measured immediately before and 15 min after inhalation. The main performance outcome was mean power output. RESULTS: After the inhalation of salbutamol, FEV1 was significantly increased by 6.4% (4.9%) versus 1.0% (4.4%) with placebo (P < 0.001). Despite this increase in FEV1, mean power output during the salbutamol time trial was not increased regardless of relative dose per kilogram of body weight and asthma status. Mean heart rate (P = 0.01), respiratory rate (P = 0.01), minute ventilation (P = 0.03) and perceived leg discomfort (P = 0.03) were significantly increased in the salbutamol condition. CONCLUSIONS: The inhalation of 1600 μg salbutamol improved FEV1 regardless of EVH status but did not improve 10-km time trial performance in trained competitive male cyclists regardless of relative dose per kilogram of body weight or EVH status. Significant increases in heart rate and minute ventilation occurred secondary to stimulation of the adrenergic nervous system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".