Do family physicians, emergency department physicians, and pediatricians give consistent sport-related concussion management advice?
Bibliographic record
Abstract
OBJECTIVE: To identify differences and gaps in recommendations to patients for the management of sport-related concussion among FPs, emergency department physicians (EDPs), and pediatricians. DESIGN: A self-administered, multiple-choice survey was e-mailed to FPs, EDPs, and pediatricians. The survey had been assessed for content validity. SETTING: Two community teaching hospitals in the greater Toronto area in Ontario. PARTICIPANTS: Two hundred seventy physicians, including FPs, EDPs, and pediatricians, were invited to participate. MAIN OUTCOME MEASURES: Identification of sources of concussion management information, usefulness of concussion diagnosis strategies, and whether physicians use common terminology when explaining cognitive rest strategies to patients after sport-related concussions. RESULTS: The response rate was 43.7%. Surveys were completed by 70 FPs, 23 EDPs, and 11 pediatricians. In total, 49% of FP, 52% of EDP, and 27% of pediatrician respondents reported no knowledge of any consensus statements on concussion in sport, and 54% of FPs, 86% of EDPs, and 78% of pediatricians never used the Sport Concussion Assessment Tool, version 2. Only 49% of FPs, 57% of EDPs, and 36% of pediatricians always advised cognitive rest. CONCLUSION: This study identified large gaps in the knowledge of concussion guidelines and implementation of recommendations for treating patients with sport-related concussions. Although some physicians recommended physical and cognitive rest, a large proportion failed to consistently advise this strategy. Better knowledge transfer efforts should target all 3 groups of physicians.
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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 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".