Analysis of doping control test results in individual and team sports from 2003 to 2015
Bibliographic record
Abstract
Background: Determining the prevalence of doping in sport might be useful for anti-doping authorities to gauge the effectiveness of anti-doping policies implemented to prevent positive attitudes toward doping. Using questionnaires and personal interviews, previous investigations have found that the prevalence of doping might be different among different sports disciplines; however, there is no sport-specific information about the proportion of adverse and atypical findings (AAF) in samples used for doping control. The aim of the present investigation was to assess the differences in the frequency of adverse analytical and atypical findings among sports using the data made available by the World Anti-Doping Agency. Methods: The data included in this investigation were gathered from the Testing Figures Reports made available annually from 2003 to 2015 by the World Anti-Doping Agency. These Testing Figures Reports include information about the number of samples analyzed, the number of AAFs reported, and the most commonly found drugs in the urine and blood samples analyzed. A total of 1,347,213 samples were analyzed from the individual sports selected for this investigation, and 698,371 samples were analyzed for disciplines catalogued as team sports. Results: In individual sports, the highest proportions of AAF were 3.3% ± 1.0% in cycling, 3.0% ± 0.6% in weightlifting, and 2.9% ± 0.6% in boxing. In team sports, the highest proportions of AAF were 2.2% ± 0.5% in ice hockey, 2.0% ± 0.5% in rugby, and 2.0% ± 0.5% in basketball. Gymnastics and skating had the lowest proportions at (≤1.0%) for individual sports, and field hockey, volleyball and football had the lowest proportions for team sports (≤1.4%). Conclusion: As suggested by the analysis, the incidence of AAF was not uniform across all sports disciplines, with the different proportions pointing to an uneven use of banned substances depending on the sport. This information might be useful for increasing the strength and efficacy of anti-doping policies in those sports with the highest prevalence in the use of banned substances.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".