A Framework for Clinicians to Improve the Decision-Making Process in Return to Sport
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Return-to-sport (RTS) decisions are critical to clinical sports medicine and are often characterised by uncertainties, such as re-injury risk, time pressure induced by competition schedule and social stress from coaches, families and supporters. RTS decisions have implications not only for the health and performance of an athlete, but also the sports organisation. RTS decision-making is a complex process, which relies on evaluating multiple biopsychosocial factors, and is influenced by contextual factors. In this narrative review, we outline how RTS decision-making of clinicians could be evaluated from a decision analysis perspective. To begin with, the RTS decision could be explained as a sequence of steps, with a decision basis as the core component. We first elucidate the methodological considerations in gathering information from RTS tests. Second, we identify how decision-making frameworks have evolved and adapt decision-making theories to the RTS context. Third, we discuss the preferences and perspectives of the athlete, performance coach and manager. We conclude by proposing a framework for clinicians to improve the quality of RTS decisions and make recommendations for daily practice and research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it