Knee Pain in Elite Dancers: A Review of Imaging Findings
Bibliographic record
Abstract
Introduction: Musculoskeletal injuries are a frequent occurrence in dancers of all skill levels, and the knee is the most common anatomical location. Our purpose was to identify the specific knee injuries encountered in a large cohort of dancers presenting to a tertiary-level dance injury clinic with knee pain. The relevant imaging findings of the identified knee injuries are highlighted. Methods: All new patients referred to the specialist dance injury clinic between March 2012 and February 2017 were entered into a database. Those with a knee-specific injury were selected with documentation of relevant demographic information. Clinic notes were analyzed for information related to a preceding acute traumatic event, and any relevant imaging was reviewed. This formed the basis for the review with Pubmed being utilized to identify relevant papers on the specific pathologies including etiology, imaging findings, and management. Results: Data from a cohort of 197 dancers presenting with a knee complaint were reviewed, composed of 144 women and 53 men with an average age of 28 years (range: 12–75 years). The most common knee complaint was anterior knee pain ( n = 111) followed by medial-side knee pain ( n = 42). The most frequent diagnoses included patellofemoral pain syndrome ( n = 69), medial meniscal injury ( n = 29), and Hoffa’s fat pad impingement ( n = 13). Conclusion: An anatomy--based approach with regard to the site of pain can be useful in identifying any potential abnormality. Knowledge of the radiological appearances of the most frequently seen knee abnormalities in dancers will aid in prompt and correct diagnosis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".