Challenges surrounding return-to-play (RTP) for the sports clinician: a case highlighting the need for a thorough three-step RTP model.
Bibliographic record
Abstract
Return-to-play (RTP) is a multifactorial process of retuning an injured athlete back to competition when risk for re-injury is minimized. Traditionally, these decisions are made by medical practitioners based on experience or anecdotal evidence. RTP decisions continue to be a challenging task for the medical practitioner. In the interest of advancing sports medicine for the betterment of athletes, improving the RTP decision-making process with a new paradigm has been suggested.1 It stands to clarify the intricacies used by clinicians when making RTP decisions by providing insight into the multiple factors that must be considered; not only by the athlete and medical practitioner, but all relevant parties (i.e., coaches, trainers, and organizations). This case describes a 19-year-old Ontario Junior Hockey League (OJHL) player who fractured his left clavicle during game play and consequently, suffered a more severe injury to the same clavicle 5½ weeks later by returning to competition against medical advice. This case highlights the potential issues that present when a RTP protocol is poorly executed and addresses the need to adopt a thorough decision-based RTP model proposed by Creighton et al.1 Further, the discussion will draw on current literature and issues surrounding RTP, and the potential legal implications associated with premature return to competition. Given the lack of consensus among sport medicine experts in regards to RTP criteria, the presented model stands to provide a pivotal framework upon which future research can be conducted, while improving the current criteria in place when returning an athlete to competition to aid medical practitioners.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".