The International Olympic Committee Venue Ultrasound Program
Bibliographic record
Abstract
ABSTRACT: The objective of this pilot investigation was to describe the novel use of venue ultrasound at the 2020 Tokyo Olympic Games. Portable laptop ultrasound machines were made available to cover seven Olympic sports at seven venues. The responses by both the National Olympic Committee personnel accompanying the medical room visits and by the examining physicians were recorded. Athletes were followed up until the end of the Olympic Games and the ultrasound diagnostic accuracy was evaluated. Fourteen athletes were evaluated using venue ultrasound and the recorded injuries included seven soft tissue, five osseous, and two nonmusculoskeletal injuries. From these, eight athletes were evaluated further by other imaging modalities, which indicated that the ultrasound provided an accurate diagnosis in all cases. All National Olympics Committee personnel reported increased diagnostic confidence and felt that venue ultrasound should be considered for future sports events. Furthermore, all evaluating physicians felt ultrasound was helpful in refining the diagnosis. The average years of sports ultrasound experience was 8.4 yrs and the average years of clinical sports medicine experience was 9.3 yrs among the physicians. In conclusion, the International Olympic Committee Venue Ultrasound Pilot Program showed promise in improving venue triaging, suggesting its role at future sports events.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".