Academic accommodation for post concussion student athletes: can physicians do better?
Bibliographic record
Abstract
Objective To determine the barriers and facilitators to the implementation of concussion related medical recommendations in a sport and school environment. Design Retrospective longitudinal chart review and structured telephone interviews. Setting A sport medicine physician's office in Ontario, Canada. Subjects 159 students with a sport-related concussion. Intervention The data collection involved a review of 170 charts of patients seen for concussion over a 5-year period (2006–2010). Two blinded authors independently reviewed each chart and SCAT or SCAT2 symptom self report form. In situations where there was discrepancy between the two reviewers results, a third author reviewed the charts. The telephone interviews supported the data collection. Outcome Measures Our primary outcome measures were (1) whether the patient returned to their sport too soon, or before they had fully recovered, and (2) whether the patient returned to school too soon, or before they had adequately recovered. The phone interview was structured to collect outcome measures providing insight into the patients' post-concussion course and recovery process. This will assist in determining what factors they believe improved their condition, or what slowed the recovery process. Results In terms of the primary outcomes, 43.53% of students returned to sport too soon and 44.71% returned to school too soon. Conclusions Currently, physicians advise restricted mental and physical activity, but no clear guideline exists explaining exactly what cognitive rest entails for post-concussion student athletes. Efforts are needed to find the best method of implementing a coordinated plan for the post-concussion athlete who is returning to school. Competing interests None.
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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 | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".