Do Canadian sport and exercise medicine physicans and emergency physicians give consistent sport-related concussion management advice?
Bibliographic record
Abstract
Objective To identify differences and gaps in the recommendations to patients for the management of sport-related concussion among sport and exercise medicine physicians and emergency physicians. Design A self-administered multiple-choice survey, which had been validated for content validity. Setting Canada. Subjects Physicians who have passed the diploma examination of the Canadian Academy of Sport and Exercise Medicine (CASEM-MDs) and physicians on the Canadian Association of Emergency Physicians (CAEP-MDs) database. Intervention The survey link was emailed to 470 CASEM-MDs and to 789 CAEP-MDs across Canada. Outcome Measures Key survey questions identified sources of concussion management information, usefulness of concussion diagnosis strategies, and whether physicians use common terminology when explaining cognitive rest strategies to patients after a sport-related concussion. Results Only 71% of CASEM-MDs usually use the SCAT2, whereas 86% of CAEP-MDs never use the SCAT2. 75% of CASEM-MDs usually advise time off of work or school after a sport-related concussion and 64% of CAEP-MDs do likewise. Only 75% of CASEM-MDs usually utilise balance testing. When queried how cognitive rest can best be achieved after a sport-related concussion, many choices were appealing with no consistent consensus. Conclusions We identified a lack of consistency in the implementation of recommendations for sport-related concussion patients. It appears that the SCAT2 is utilised more in the office setting than in the emergency department. More recent strategies such as balance testing have not gained consistent usage, even among physicians with recognised expertise in concussion management. Better knowledge translation efforts should target both of these groups of physicians. Acknowledgements Special thanks go to Paul Krueger, Leigh Hayden and the other 18 participants in the validation of our survey. 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 | 0.007 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".