Diagnostic ultrasound of muscle injuries: what the sports medicine clinician should know
Bibliographic record
Abstract
Muscle injuries are among the most prevalent musculoskeletal conditions in athletes, contributing significantly to morbidity and time lost from competition. The use of ultrasound (US) is advantageous in assessing these injuries due to its low cost, accessibility, portability, dynamic real-time capabilities and utility in prognosis and rehabilitation planning. This state-of-the-art review offers a comprehensive synthesis of current evidence on the anatomical, technical and clinical aspects of diagnostic US in evaluating sports-related muscle injuries. Key topics include the differentiation between direct and indirect injury mechanisms, classification systems, prognostic indicators and common complications such as fibrosis, haematoma and myositis ossificans. Emphasis is placed on a practical, stepwise approach to US examination and reporting, incorporating anatomical detail and functional assessment to support individualised return-to-play decisions. Despite certain limitations, the US remains a cornerstone imaging modality in sports medicine. Emerging technologies, including advanced imaging techniques, hold promise for enhancing diagnostic accuracy and optimising clinical outcomes.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".