The epidemiology of MRI detected shoulder injuries in athletes participating in the Rio de Janeiro 2016 Summer Olympics
Bibliographic record
Abstract
BACKGROUND: To use Magnetic Resonance Imaging (MRI) to characterize the severity, location, prevalence, and demographics of shoulder injuries in athletes at the Rio de Janeiro 2016 Summer Olympic Games. METHODS: This was a retrospective analysis of all routine shoulder MRIs obtained from the Olympic Village Polyclinic during the Rio 2016 Summer Olympics. Imaging was performed on 1.5 T and 3 T MRI, and interpretation was centrally performed by a board-certified musculoskeletal radiologist. Images were assessed for tendon, muscle, bone, bursal, joint capsule, labral, and chondral abnormality. RESULTS: A total of 11,274 athletes participated in the Games, of which 55 (5%) were referred for a routine shoulder MRI. Fifty-three (96%) had at least two abnormal findings. Seven (13%) had evidence of an acute or chronic anterior shoulder dislocation. Forty-nine (89%) had a rotator cuff partial tear and / or tendinosis. Subacromial / subdeltoid bursitis was present in 29 (40%). Thirty (55%) had a tear of the superior labrum anterior posterior (SLAP). CONCLUSION: Our study demonstrated a high prevalence of both acute and chronic shoulder injuries in the Olympic athletes receiving shoulder MRI. The high rates of bursal, rotator cuff, and labral pathology found in these patients implies that some degree of glenohumeral instability and impingement is occurring, likely due to fatigue and overuse of the dynamic stabilizers. Future studies are needed to better evaluate sport-specific trends of injury.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".