Analgesic use in sports—results of a systematic literature review
Bibliographic record
Abstract
BACKGROUND: Consumption of medication to alleviate pain is widespread in Germany. Around 1.9 million men and women take analgesics every day; some 1.6 million persons are addicted to painkillers. Analgesic use is thought also to be common in sports, even in the absence of pain. The aim of this study was to assess the extent of painkiller use among athletes. METHODS: In line with the PRISMA criteria and the modified PICO(S) criteria, a systematic literature review was registered (Openscienceframework, https://doi. org/10.17605/OSF.IO/VQ94D) and carried out in PubMed and SURF. The publications identified (25 survey studies, 12 analyses of doping control forms, 18 reviews) were evaluated in standardized manner using the Newcastle Ottawa Scale (NOS) and AMSTAR (A MeaSurement Tool to Assess systematic Reviews). RESULTS: Analgesic use is widespread in elite sports. The prevalence varies between 2.8% (professional tennis) and 54.2% (professional soccer). Pain medication is also taken prophylactically in the absence of symptoms in some non-elite competitive sports. In the heterogeneous field of amateur sports the data are sparse and there is no reliable evidence of wide-reaching consumption of painkillers. Among endurance athletes, 2.1% of over 50 000 persons stated that they used analgesics at least once each month in connection with sports. CONCLUSION: Analgesic use has become a problem in many areas of professional/ competitive sports, while the consumption of pain medication apparently remains rare in amateur sports. In view of the increasing harmful use of or even addiction to painkillers in society as a whole, there is a need for better education and, above all, restrictions on advertising.
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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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".