Analgesics use in competitive triathletes: its relationship to doping and on predicting its usage
Bibliographic record
Abstract
The two major objectives of this study were (i) to assess variables that predict the use of analgesics in competitive athletes and (ii) to test whether the use of analgesics is associated with the use of doping. A questionnaire primarily addressing the use of analgesics and doping was distributed among 2,997 triathletes. Binary logistic regression analysis was performed to predict the use of analgesics. Moreover, the randomised response technique (RRT) was used to estimate the prevalence of doping in order to assess whether users of analgesics have a higher potential risk for doping than non-users. Statistical power analyses were performed to determine sample size. The bootstrap method was used to assess the statistical significance of the prevalence difference for doping between users and non-users of analgesics. Four variables from a pool of 16 variables were identified that predict the use of analgesics. These were: "version of questionnaire (English)", "gender (female)", "behaviour in case of pain (continue training)", and "hours of training per week (>12 h/week)". The 12-month prevalence estimate for the use of doping substances (overall estimate 13.0%) was significantly higher in athletes that used analgesics (20.4%) than in those athletes who did not use analgesics (12.4%). The results of this study revealed that athletes who use analgesics prior to competition may be especially prone to using doping substances. The predictors of analgesic use found in the study may be of importance to prepare education material and prevention models against the misuse of drugs in 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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".