Return-to-Play Decisions: Are They the Team Physician's Responsibility?
Bibliographic record
Abstract
OBJECTIVE: Return-to-play (RTP) decisions are a central component of the Team Physician's clinical work, yet there is little more than anecdotal reference to these in the literature. We recently published a 3-step model for return-to-play medical decision making and, in the current paper, undertook a systematic review of the literature to determine the level of evidence in support of this model. DATA SOURCES: PubMed, Web of Science, and CINAHL electronic databases. Any article specifically related to concussion, head injuries, neck injuries, illness, medical conditions (including cardiovascular and renal), and preparticipation in sport or that reported RTP as a clinical outcome was excluded. Any article that contained a discussion on one of the components of the 3-step decision-based RTP model was included. RESULTS: We reviewed 148 articles that met the criteria for inclusion and found 98 review articles, 39 original articles, 6 case reports, and 5 editorials. Of these, 141 articles mentioned Step 1 of the medical decision-making process for RTP (Medical Factors), 26 mentioned Step 2 (Sport Risk Modifiers), and 20 mentioned Step 3 (Decision Modifiers). Of the 148 articles in total, only 13 focused on RTP as the main subject and the remaining 135 mentioned RTP anecdotally. Of these 13 articles, 5 were reviews, 4 were editorials, and 4 were original research. CONCLUSIONS: Although 148 articles we retrieved mention RTP in relation to a specific injury, medical condition, or specific topic, only 13 articles focused specifically on the RTP decision-making process, and 6 of 13 were restricted to Step 1 of the 3-step model (Medical Factors). Return-to-play is a fertile field for research and thought leadership beginning with a focus on the Team Physician's appropriate role in RTP decision making, particularly considering the factors identified in Step 3 (Decision Modification).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".