Compliance with needle-use declarations at two Olympic Winter Games: Sochi (2014) and PyeongChang (2018)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it