Urine concentrations of oral salbutamol in samples collected after intense exercise in endurance athletes
Bibliographic record
Abstract
Our objective was to investigate urine concentrations of 8 mg oral salbutamol in samples collected after intense exercise in endurance athletes. Nine male endurance athletes with a VO2max of 70.2 ± 5.9 mL/min/kg (mean ± SD) took part in the study. Two hours after administration of 8 mg oral salbutamol, subjects performed submaximal exercise for 15 min followed by two, 2-min exercise bouts at an intensity corresponding to 110% of VO2max and a bout to exhaustion at same intensity. Urine samples were collected 4, 8, and 12 h following administration of salbutamol. Samples were analyzed by the Norwegian World Anti-doping Agency (WADA) laboratory. Adjustment of urine concentrations of salbutamol to a urine specific gravity (USG) of 1.020 g/mL was compared with no adjustment according to WADA's technical documents. We observed greater (P = 0.01) urine concentrations of salbutamol 4 h after administration when samples were adjusted to a USG of 1.020 g/mL compared with no adjustment (3089 ± 911 vs. 1918 ± 1081 ng/mL). With the current urine decision limit of 1200 ng/mL for salbutamol on WADA's 2013 list of prohibited substances, fewer false negative urine samples were observed when adjusted to a USG of 1.020 g/mL compared with no adjustment. In conclusion, adjustment of urine samples to a USG of 1.020 g/mL decreases risk of false negative doping tests after administration of oral salbutamol. Adjusting urine samples for USG might be useful when evaluating urine concentrations of salbutamol in doping cases.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".