Do family physicans, emergency physicians and paediatricians 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 family physicians (FPs), emergency physicians (ERs) and paediatricians (Peds). Design A self-administered multiple-choice survey, which had been validated for content validity. Setting Toronto East General Hospital and Markham Stouffville Hospital, both in Ontario, Canada. Subjects 270 physicians were invited to participate; 104 surveys were completed (39%). Intervention The survey link was emailed to FPs, ERs and Peds from the above hospitals. Data were analysed using SPSS. Frequencies and percentages were calculated with cross-tabulation of data by specialty. 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 For making a diagnosis of concussion, 54% of FPs, 86% of ERs and 78% of Peds never used the SCAT2; 49% of FPs, 57% of ERs and 36% of Peds always advised cognitive rest, the mainstay of early management for post-concussion symptoms; 65% of FPs, 71% of ERs and 55% of Peds always advised physical rest; and 49% of FPs, 52% of ERs and 27% of Peds reported no knowledge of any consensus statements on concussion in sport. Conclusions We identified large gaps in the implementation of recommendations for sport-related concussion patients. Although some physicians are recommending physical and cognitive rest, a large proportion fail to consistently advise this strategy. Better knowledge translation efforts should target all three groups of physicians. Acknowledgements Thank you to Babak Aliarzadeh for the statistical analysis. 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 | 0.009 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".