UNDERSTANDING HOW CLINICIANS AND ATHLETES MAKE RETURN TO PLAY DECISIONS – TOWARDS PREVENTING RE INJURIES
Bibliographic record
Abstract
Background Clinicians and athletes do not always agree when return to play (RTP) is advisable. A better understanding of how RTP decisions are made could help prevent re injury. Objective To describe and compare risk assessments from sport medicine physicians, physiotherapists and athletes during routine care of injured athletes, and to elicit if there were factors that influenced their risk tolerance. Design Feasibility study to establish this method of examining the way athletes and clinicians make RTP decisions. Setting Specialized orthopedic and sports medicine hospital in Doha, Qatar. Patients We recruited the physician, the physiotherapist, and the athlete they were caring for (“triplet”) to participate. Triplets were recruited from an acute groin injury study and an acute hamstring injury study. Of the 15 athletes approached, 5 were excluded because we obtained risk estimates from only 1 stakeholder. Of the remaining 10 cases, we obtained risk estimates from 7 triplets and 3 doublets. Outcome measures Participants provided their own estimated probability distribution for risk of re-injury within the subsequent 2 months after RTP, based on the available knowledge. In addition, we asked participants about factors that influenced their risk tolerance for the particular case. Results The figure shows the risk estimations in 2 of the 10 cases, illustrating that there were clear and considerable differences in risk estimates. Figure 1 Factors that modified risk tolerance in at least three participants were “timing and season”, “pressure from athlete” and “external pressure”. Overall, risk modifiers influenced the decision in 13 of 27 possible cases. Conclusion Clinicians and athletes with access to the same information can have considerably different estimates for the risk of reinjury. Risk tolerance in the real world is sometimes modified by external factors.
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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 | 0.024 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".