A painful dilemma? Analgesic use in sport and the role of anti-doping
Bibliographic record
Abstract
How clinicians should address the use and abuse of analgesics in sport has been a focus of debate for many years. Concern for an athlete’s health and a desire to prevent unfair distortion of performance underlie any discussion of this issue. In 1967 the original IOC list of prohibited substances specifically identified ‘Narcotic Analgesics’ as being prohibited in sport. The 2017 WADA Prohibited List (List) mandates that ‘Narcotics’ and ‘Cannabinoids’ are prohibited ‘In-Competition’. More commonly used analgesics, including non-steroidal anti-inflammatory drugs, paracetamol, local anaesthetics, and some weak opioids such as tramadol and codeine are not prohibited. No well-defined boundary separates either the health risk or ergogenic potential of cannabinoids and narcotics versus the more commonly used analgesics. Should more analgesics be added to the List or should narcotics and cannabis be removed? Is the use of pain medication doping? As defined by Article 2 of the World Anti-Doping Code (Code),1 doping is defined, inter alia, as the presence, use, possession or trafficking of a prohibited substance. This leads one to query: what are the key determinants for inclusion of a substance on the List? The Code criteria for the …
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.028 | 0.032 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".