SPORT CONCUSSION KNOWLEDGE BASE AND CURRENT PRACTICE– A SURVEY OF PHYSICIAN SECTIONS FROM THE ONTARIO MEDICAL ASSOCIATION
Bibliographic record
Abstract
Background It is critical that physicians understand concussion management. Objective Identify practice patterns/knowledge base in two physician populations. Design On-line survey. Setting Ontario, Canada. Participants Physicians from Sections: Sport and Exercise Medicine (SEM), General and Family Practice (SGFP). Interventions Emailed survey, 2 reminders. Main utcome measurements: Practice patterns/knowledge base, learning methods: current/preferred. Results Participants:SEM 92/594 (15.5%), SGFP 270/12,168 (2.2%); urban practice (90.2% SEM, 71.5% SFGP; P<.001). In preceding 3 months, 84.8% of SEM and 65.6% of SFGP had managed patients with concussion. More SEM than SGFP physicians saw >5 children under 18 with concussions per month (40.2% SEM, 9.5% SGFP; P<001).Tools:Clinical examination (92.4% SEM, 93.7% SFGP); Sport Concussion Assessment Tool (SCAT/SCAT2) (68.4% SEM, 34.1% SFGP; P<.001); balance testing (56.5% SEM, 37.4% SFGP; P=.001); computerized neurocognitive testing (23.9% SEM, 1.9% SFGP; P<.001); concussion grading scales (9.8% SEM, 14.1% SFGP; P<.001).Management:Complete physical rest (65.2% SEM, 68.5% SFGP); absolute cognitive rest (46.7% SEM, 51.9% SFGP); modified school/work until symptom resolution (50.0% SEM, 38.5% SFGP; P=.026); no cognitive rest (3.2% SEM, 9.6% SGFP; P=.026).Return-to-play:Clinical examination (87.0% SEM, 82.6% SFGP); SCAT/SCAT2 (60.8% SEM, 29.6% SFGP; P<.001); balance testing (56.5% SEM, 37.4% SFGP; P<.001); computerized neurocognitive testing (35.9% SEM, 2.2% SFGP; P<.001); concussion grading scales (7.6% SEM, 9.6% SFGP).Current learning sources:colleagues (55.4% SEM, 27.8% SFGP; P<.001); specialists (33.7% SEM, 23.7% SFGP; P=.030); continuing medical education (CME) courses/conferences (67.4% SEM, 54.7% SFGP; P=.017); journals/publications (48.9% SEM, 25.2% SFGP; P<.001); websites (35.8% SEM, 32.2% SFGP); medical school/residency training (19.6% SEM, 17.4% SFGP).Preferred learning sources:CME courses/conferences (85.9% SEM, 73.9% SFGP; P=.006); websites (35.9% SEM, 47.8%, SFGP; P=.024); medical school/residency training (37.0% SEM, 47.8% SFGP). Conclusions Gaps exist between consensus-based recommendations regarding concussions and current clinical practice patterns. Enhanced training in medical school/residency and additional CME initiatives are recommended.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".