Medical and pharmacological approach to adjust the salbutamol anti-doping policy in athletes
Bibliographic record
Abstract
BACKGROUND: Salbutamol abuse detection by athletes is based on a urinary upper threshold defined by the World Anti-Doping Agency (WADA). However, this threshold was determined in healthy, untrained individuals and after a dose of salbutamol inhaled that might not really mirror the condition of asthmatic athletes and the experts's guidelines for asthma management. We aimed to revise this threshold in accordance with recommended clinical practice (that appear to be different from the actual WADA recommendation) and in exercise conditions. METHODS: For the present open-label design study, we included 12 trained male cyclists (20 to 40 y/o) with asthma. Differently from the previous pharmacokinetic study supporting the actual salbutamol urinary upper threshold, we decided to administer a close to recommended clinical practice daily dose of 3x200 μg.d(-1) inhaled salbutamol (instead of 1600 μg.d(-1) as authorized by the anti-doping policy). Urine salbutamol concentration was quantified by liquid chromatography-tandem ion trap mass spectrometry and corrected for urine density, at rest and after a 90-min cycling effort at 70-80 % of the maximal aerobic power. RESULTS: The maximum urine salbutamol concentration value peaked after the cycling effort and was 510 ng.mL(-1). That is twice lower than the actual WADA threshold to sanction salbutamol abuse, this "legal" threshold being based on pharmacokinetic data after a daily dose that is 8 fold the total dose sequentially administrated in our study. Considering its 95 % confidence interval, this threshold value could be more stringent. CONCLUSION: By using conditions in accordance with the experts' clinical and safety guidelines for asthma management in athletes undergoing an intense exercise bout, our study suggests that the urine salbutamol concentration threshold could be lowered to redefine the rule supporting the decision to sanction an athlete for salbutamol abuse.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".