Should We be Concerned with Nicotine in Sport? Analysis from 60,802 Doping Control Tests in Italy
Bibliographic record
Abstract
BACKGROUND: Nicotine is a psychostimulant drug with purported use in sports environments, though the use of nicotine among athletes has not been studied extensively. OBJECTIVE: The aim of this study was to assess the nicotine positivity rate in 60,802 anti-doping urine samples from 2012 to 2020. METHODS: Urine samples obtained in-competition at different national and international sports events held in Italy during the period 2012-2020 were analysed. All samples were from anonymous athletes that were collected and analysed at the WADA-accredited antidoping laboratory in Rome, Italy. Samples were analysed by gas chromatography coupled with mass spectrometry, with a cut-off concentration for nicotine of > 50 ng/mL. Results were stratified by year, sport and sex. RESULTS: An overall mean of 22.7% of the samples (n = 13,804; males: n = 11,099; females: n = 2705) showed nicotine intake, with male samples also displaying higher positivity rates than female (24.1% vs 18.5%). Sample positivity was higher during 2012-2014 (25-33%) than 2015-2020 (15-20%). Samples from team sports displayed a higher positivity rate than those from individual sports (31.4 vs 14.1%). CONCLUSIONS: The current data demonstrates that one in five samples from a range of 90 sports test positive for nicotine in-competition. There is a lower positivity rate in endurance versus power/strength athletes and higher positivity rate in team versus individual sports, probably accounted for by differences in physiological and psychological demands and the desire for socialisation. WADA, international and national sports federations should consider these findings with concern, proactively investigate this phenomenon and act in order to protect the health and welfare of its athletes.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".