Practice management of musculoskeletal injuries in active children
Bibliographic record
Abstract
BACKGROUND: Although increasing participation in physical activities has significant health benefits, there are no guidelines to help professionals decide when it is safe to return to activity after injury. OBJECTIVE: To examine the specific criteria (eg, strength, pain) that expert sport medicine clinicians use for return to activity decisions in children with musculoskeletal injuries. METHODS: The authors conducted an online cross-sectional survey of certified Canadian sport medicine doctors (MDs) and sport rehabilitation specialists (physiotherapists (PTs) or athletic therapists (ATs)). The authors asked how they would measure each of the following signs in the context of a knee injury: sport-specific skills, pain, swelling, strength, range of motion (ROM) and balance. Clinicians also ranked the importance of each sign with respect to influencing their recommendations for each of five clinical vignettes. RESULTS: The overall response rate was 33.6% (464/1380) with similar rates for each profession. For each clinical sign, all three professions preferred the same measure to determine readiness to return to play: standardised testing for sport-specific skills, impact on function for pain, palpation for swelling, manual muscle testing for strength, visual inspection for ROM and standing on one leg with eyes closed for balance. Regarding importance of specific signs for return to activity, all professions had similar responses for one vignette, but MDs differed from PTs and ATs for the remaining four. Finally, pain was ranked as the no 1 or 2 most important sign in all five vignettes by 41.0% of MDs, 18.1% of ATs and 11.3% of PTs, whereas sport-specific skills was chosen by 9.6% MDs, 12.0% ATs and 16.1% PTs. CONCLUSION: Our results provide the foundation for future work leading towards the development of interdisciplinary consensus guidelines.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".