Prevalence of caffeine use in elite athletes following its removal from the World Anti-Doping Agency list of banned substances
Bibliographic record
Abstract
The aim of this investigation was to determine the use of caffeine by athletes after its removal from the World Anti-Doping Agency list. For this purpose, we measured the caffeine concentration in 20 686 urine samples obtained for doping control from 2004 to 2008. We utilized only urine samples obtained after official national and international competitions. Urine caffeine concentration was determined using alkaline extraction followed by gas chromatography-mass spectrometry. The limit of detection (LOD) was set at 0.1 µg·mL(-1). The percentage of urine samples below the LOD was 26.2%; the remaining 73.8% of the urine samples contained caffeine. Most urine samples (67.3%) had urinary caffeine concentrations below 5 µg·mL(-1). Only 0.6% of urine samples exceeded the former threshold for caffeine doping (12 µg·mL(-1)). Triathlon (3.3 ± 2.2 µg·mL(-1)), cycling (2.6 ± 2.0 µg·mL(-1)), and rowing (1.9 ± 1.4 µg·mL(-1)) were the sports with the highest levels of urine caffeine concentration; gymnastics was the sport with the lowest urine caffeine concentration (0.5 ± 0.4 µg·mL(-1)). Older competitors (>30 y) had higher levels of caffeine in their urine than younger competitors (<20 y; p < 0.05); there were no differences between males and females. In conclusion, 3 out of 4 athletes had consumed caffeine before or during sports competition. Nevertheless, only a small proportion of these competitors (0.6%) had a urine caffeine concentration higher than 12 µg·mL(-1). Endurance sports were the disciplines showing the highest urine caffeine excretion after competition.
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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 | 0.001 |
| 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".