Clinical Management of Musculoskeletal Injuries in Active Children and Youth
Bibliographic record
Abstract
OBJECTIVE: To describe how different health care specialists manage musculoskeletal injury in children and examine factors influencing return to play decisions. DESIGN: National survey. SETTING: Secure Web site hosting online questionnaire. PARTICIPANTS: Medical doctors, physical therapists, and athletic therapists who were members of their respective sport medicine specialty organizations. INDEPENDENT VARIABLES: Professional affiliation and the effect of the following factors were examined: pushy parent, cautious parent, protective equipment, previous injury, musculoskeletal maturity, game importance, position played, team versus individual sport, and time since injury. MAIN OUTCOME MEASURES: Recommendation of return to activity after common injuries seen in children and adolescents as described in 5 vignettes; consistency of responses across vignettes. RESULTS: The survey was completed by 464 respondents (34%). There were several differences between the professional groups in their recommendations to return to activity. Most factors studied did not tend to influence the decision to return to activity, although protective equipment often increased the response to return sooner. The number of participants who would return a child to activity sooner or later for each factor varied greatly across the 5 vignettes, except for pushy parent or cautious parent. CONCLUSIONS: Management practices of sport medicine clinicians vary according to profession, child, clinical factors, and sport-related factors. Decisions regarding return to play vary according to 5 specific characteristics of each clinical case. These findings help establish areas of consensus and disagreement in the management of children with injuries and safe return to physical activity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".