Are Canadian clinicians providing consistent sport-related concussion management advice?
Bibliographic record
Abstract
OBJECTIVE: To compare the knowledge and use of recommendations for the management of sport-related concussion (SRC) among sport and exercise medicine physicians (SEMPs) and emergency department physicians (EDPs) to assess the success of SRC knowledge transfer across Canada. DESIGN: A self-administered, multiple-choice survey accessed via e-mail by SEMPs and EDPs. The survey had been assessed for content validity. SETTING: Canada. PARTICIPANTS: The survey was completed between May and July 2012 by SEMPs who had passed the diploma examination of the Canadian Academy of Sport and Exercise Medicine and by EDPs who did not hold this diploma. MAIN OUTCOME MEASURES: Knowledge and identification of sources of concussion management information, use of concussion diagnosis strategies, and whether physicians use common and consistent terminology when explaining cognitive rest strategies to patients after an SRC. RESULTS: There was a response rate of 28% (305 of 1085). The SEMP and EDP response rates were 41% (147 of 360) and 22% (158 of 725), respectively. Of the responses, 41% of EDPs and 3% of SEMPs were unaware of any consensus statements on concussion in sport; 74% of SEMPs used the Sport Concussion Assessment Tool, version 2 (SCAT2), "usually or always," whereas 88% of EDPs never used the SCAT2. When queried about how cognitive rest could best be achieved after an SRC, no consistent answer was documented. CONCLUSION: Differences and a lack of consistency in the implementation of recommendations for SRC patients were identified for SEMPs and EDPs. It appears that the SCAT2 is used more in the SEMP setting than in the emergency context. Further knowledge transfer efforts and research should address the barriers to achieving more consistent advice given by physicians who attend SRC patients.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".