Should We be Concerned with Nicotine in Sport? Analysis from 60,802 Doping Control Tests in Italy
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it