Team Clinician Variability in Return-to-Play Decisions
Bibliographic record
Abstract
OBJECTIVE: To describe the variability in the return-to-play (RTP) decisions of experienced team clinicians and to assess their clinical opinion as to the relevance of 19 factors described in a RTP decision-making model. DESIGN: Survey questionnaire. SETTING: Advanced Team Physician Course. PARTICIPANTS: Sixty-seven of 101 sports medicine clinicians completed the questionnaire. MAIN OUTCOME MEASURES: Results were analyzed using descriptive statistics. For categorical variables, we report percentage and frequency. For continuous variables, we report mean (SD) if data were approximately normally distributed and frequencies for clinically relevant categories for skewed data. RESULTS: The average number of years of clinical sports medicine experience was 13.6 (9.8). Of the 62 clinicians who responded fully, 35% (n = 22) would "clear" (vs "not clear") an athlete to participate in sport even if the risk of an acute reinjury or long-term sequelae is increased. When respondents were given 6 different RTP options rather than binary choices, there were increased discrepancies across some injury risk scenarios. For example, 8.1% to 16.1% of respondents who chose to clear an athlete when presented with binary choices, later chose to "not clear" an athlete when given 6 graded RTP options. The respondents often considered factors of potential importance to athletes as nonimportant to the RTP decision process if risk of reinjury was unaffected (range, n = 4 [10%] to n = 19 [45%]). CONCLUSIONS: There is a high degree of variability in how different clinicians weight the different factors related to RTP decision making. More precise definitions decrease but do not eliminate this variability.
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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 | 0.018 | 0.140 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".