RETURN-TO-PLAY FOLLOWING INJURY: WHOSE DECISION SHOULD IT BE?
Bibliographic record
Abstract
Background Return-to-play (RTP) decision-making is required for every injured athlete. However, conflict can and often does emerge between sport medicine clinicians, athletes, coaches and sport associations when engaging in recommended shared decision making processes. Objective This study explores differences in professionals' opinion about which criteria should be used for RTP decisions, and who is best able to evaluate them. Design Cross-sectional survey. Setting Canada. Participants Canadian sport medicine physicians, physiotherapists, athletic therapists, chiropractors, massage therapists, athletes, coaches and representatives from the Canadian Olympic Committee, Canada Games, and Canadian Soccer Association. Risk factor assessment None. Main outcome measurements Descriptive analysis of a 10-min online survey that asked respondents to rate criteria as mandatory to irrelevant on a 5-point Likert scale, and to indicate which profession was best able to evaluate the criteria. Results In general, medical doctors, physiotherapists and athletic therapists were considered best able to assess factors related to risk of injury and complications from injury. Each clinician group (except sport massage therapists) generally believed their own profession has the best capacity to evaluate the criteria. Athletes, coaches and sport associations were considered to have the best capacity to assess factors related to competition (desire, psychological and financial impact, and loss of competitive standing). There remained considerable heterogeneity both between and within stakeholder groups. Conclusions We found that differences in approach to RTP decisions were generally greater within stakeholder groups compared to between stakeholder groups. If shared decision making is to become the norm in clinical sport medicine, we will need to begin a more fulsome discussion on which discrepancies can be addressed by education and research, and which simply reflect the divergence of values among different individuals.
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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 | 0.006 | 0.036 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".