Return-to-Play in Sport: A Decision-based Model
Bibliographic record
Abstract
OBJECTIVE: Return-to-play (RTP) decisions are fundamental to the practice of sports medicine but vary greatly for the same medical condition and circumstance. Although there are published articles that identify individual components that go into these decisions, there exists neither quantitative criteria nor a model for the sequence or weighting of these components within the medical decision-making process. Our objective was to develop a decision-based model for clinical use by sports medicine practitioners. DATA SOURCES: English literature related to RTP decision making. MAIN RESULTS: We developed a 3-step decision-based RTP model for an injury or illness that is specific to the individual practitioner making the RTP decision: health status, participation risk, and decision modification. In Step 1, the Health Status of the athlete is assessed through the evaluation of Medical Factors related to how much healing has occurred. In Step 2, the clinician evaluates the Participation Risk associated with participation, which is informed by not only the current health status but also by the Sport Risk Modifiers (eg, ability to protect the injury with padding, athlete position). Different individuals are expected to have different thresholds for "acceptable level of risk," and these thresholds will change based on context. In Step 3, Decision Modifiers are considered and the decision to RTP or not is made. CONCLUSIONS: Our model helps clarify the processes that clinicians use consciously and subconsciously when making RTP decisions. Providing such a structure should decrease controversy, assist physicians, and identify important gaps in practice areas where research evidence is lacking.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".