VALIDATING THE 3-STEP RETURN TO PLAY DECISION MAKING MODEL
Bibliographic record
Abstract
Background A recent return to play decision-making (RTP-DM) model for sport medicine has organized the underlying concepts into 3 steps but has not yet been validated. Objective To examine the validity of the 3-step RTP-DM model recently proposed. Design Repeated measures cross-over survey design. Setting World-wide. Participants American College of Sports Medicine clinicians involved in RTP-DM. Risk factor assessment We provided clinical vignettes of injuries and illnesses in athletes to participants through an online survey. Each vignette included examples of 3 factor types: increasing injury severity, changing risk associated with sport (e.g. different positions), and changing non-injury risk factors (e.g. financial considerations). Main Outcome Measurements For each vignette, participants indicated the level of activity restriction they would recommend (6 options from No Restrictions to No Activity) in accordance with the risk they placed on continued participation. We analyzed the data using multiple regression, adjusting for the correlated participant outcomes, to measure how changes in factors affected individual participants. Results The estimated participation rate for those involved in RTP decisions was 24.7%. As expected, we found that clinicians increase restrictions as injury severity increases. We also found that changing factors related to sport risk, and changing factors that are unrelated to sport risk will affect RTP decisions, although the effect is context-dependent and does not occur equally across all injury severities and clinical cases. The effect was also observed in each subgroup examined that included sex, age, specialty, region of training, academic status, and years of experience making RTP decisions. Conclusions Our findings that clinicians from a wide variety of backgrounds will change RTP recommendations based on clinical vignettes with changing injury severity, sport risk modifiers and decision modifiers provides evidentiary support for the 3-step model for RTP decision making recently proposed.
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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 | 0.028 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".