The influence of exercise and dehydration on the urine concentrations of salbutamol after inhaled administration of 1600 µg salbutamol as a single dose in relation to doping analysis
Bibliographic record
Abstract
The present study investigated the influence of exercise and dehydration on the urine concentrations of salbutamol after inhalation of that maximal permitted (1600 µg) on the 2015 World Anti-Doping Agency (WADA) prohibited list. Thirteen healthy males participated in the study. Urine concentrations of salbutamol were measured during three conditions: exercise (EX), exercise+dehydration (EXD), and rest (R). Exercise consisted of 75 min cycling at 60% of VO2max and a 20-km time-trial. Fluid intake was 2300, 270, and 1100 mL during EX, EXD, and R, respectively. Urine samples of salbutamol were collected 0-24 h after drug administration. Adjustment of urine concentrations of salbutamol to a specific gravity (USG) of 1.020 g/mL was compared with no adjustment. The 2015 WADA decision limit (1200 ng/mL) for salbutamol was exceeded in 23, 31, and 10% of the urine samples during EX, EXD, and R, respectively, when unadjusted for USG. When adjusted for USG, the corresponding percentages fell to 21, 15, and 8%. During EXD, mean urine concentrations of salbutamol exceeded (1325±599 ng/mL) the decision limit 4 h after administration when unadjusted for USG. Serum salbutamol Cmax was lower (P<0.01) for R(3.0±0.7 ng/mL) than EX(3.8±0.8 ng/mL) and EXD(3.6±0.8 ng/mL). AUC was lower for R (14.1±2.8 ng/mL·∙h) than EX (16.9±2.9 ng/mL·∙h)(P<0.01) and EXD (16.1±3.2 ng/mL·∙h)(P<0.05). In conclusion, exercise and dehydration affect urine concentrations of salbutamol and increase the risk of Adverse Analytical Findings in samples collected after inhalation of that maximal permitted (1600 µg) for salbutamol. This should be taken into account when evaluating doping cases of salbutamol. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 | 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".