Classification of myo-connective tissue injuries for severity grading and return to play prediction: A scoping review
Bibliographic record
Abstract
OBJECTIVES: To conduct a systematic literature search to identify currently used classifications of acute non-contact muscle injuries in sporting adults. DESIGNS: Scoping review. METHODS: A systematic literature search from January 1, 2010 to April 19, 2022 of Medline and SPORTDiscus yielded 13,426 articles that were screened for eligibility. Findings from included studies were qualitatively synthesized. Classifications and their grading, as well as outcomes and definitions were extracted. RESULTS: Twenty-four classifications were identified from the 37 included studies, most of which had low evidence study designs. Majority (57 %) of classifications were published after 2009 and were mostly developed for hamstring or other lower limb injuries. The six most cited classifications accounted for 70 % of the reports (BAMIC, modified Peetrons, Munich, Cohen, Chan and MLG-R). Outcome reporting was sparse, making it difficult to draw conclusions. Still, significant relationships between grading and time to return to play were reported for the BAMIC, modified Peetrons, Munich and Cohen classifications. Other classifications either had a very low number of reported associations, reported no associations, reported inconclusive associations, or did not report an assessment of the association. Other outcomes were poorly investigated. CONCLUSIONS: There is no agreed-upon use of muscle classification, and no consensus on definitions and terminology. As a result, reported outcomes and their relationship to severity grading are inconsistent across studies. There is a need to improve the generalizability and applicability of existing classifications and to refine their prognostic value. High-level evidence studies are needed to resolve these inconsistencies.
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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 | 0.023 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.040 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".