Compliance with needle-use declarations at two Olympic Winter Games: Sochi (2014) and PyeongChang (2018)
Bibliographic record
Abstract
OBJECTIVES: We describe compliance with the 'IOC Needle Policy' at two Winter Olympic Games (Sochi and PyeongChang) and compare these findings to those of the Summer Olympic Games of Rio de Janeiro. METHOD: All needle-use declaration(s) (NUD) received during the course of the 2014 and 2018 Olympic Games were reviewed. We recorded socio-demographic data, the nature and purpose of needle use, product(s) injected, and route of administration. Data were analysed descriptively. RESULTS: In total, doctors from 22 National Olympic Committees (NOCs) submitted 122 NUD involving 82 athletes in Sochi; in PyeongChang, doctors from 19 NOCs submitted 82 NUD involving 61 athletes. This represented approximately 2% of all athletes at both Games, and 25% and 20% of all NOCs participating in Sochi and PyeongChang, respectively. No marked differences in the NUD distribution patterns were apparent when comparing the two Winter Olympic Games. The most commonly administered substances were as follows: local anaesthetics, non-steroidal anti-inflammatory drug and glucocorticoids. Physicians submitted multiple NUD for 24% of all athletes who required a NUD. CONCLUSION: A limited number of NOCs submitted NUD suggesting a low incidence of needle use or limited compliance (approximately 2%). A key challenge for the future is to increase the rate of compliance in submitting NUD. More effective education of NOCs, team physicians and athletes regarding the NUD policy, its purpose, and the necessity for NUD submissions, in association with the enforcement of the appropriate sanctions following non-compliance are needed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".