Sport concussion knowledge base, clinical practises and needs for continuing medical education: a survey of family physicians and cross-border comparison
Bibliographic record
Abstract
CONTEXT: Evolving concussion diagnosis/management tools and guidelines make Knowledge Transfer and Exchange (KTE) to practitioners challenging. OBJECTIVE: Identify sports concussion knowledge base and practise patterns in two family physician populations; explore current/preferred methods of KTE. DESIGN: A cross-sectional study. SETTING: Family physicians in Alberta, Canada (CAN) and North/South Dakota, USA. PARTICIPANTS: CAN physicians were recruited by mail: 2.5% response rate (80/3154); US physicians through a database: 20% response rate (109/545). INTERVENTION/INSTRUMENT: Online survey. MAIN AND SECONDARY OUTCOME MEASURES: Diagnosis/management strategies for concussions, and current/preferred KTE. RESULTS: Main reported aetiologies: sports/recreation (52.5% CAN); organised sports (76.5% US). Most physicians used clinical examination (93.8% CAN, 88.1% US); far fewer used the Sport Concussion Assessment Tool (SCAT1/SCAT2) and balance testing. More US physicians initially used concussion-grading scales (26.7% vs 8.8% CAN, p=0.002); computerised neurocognitive testing (19.8% vs 1.3% CAN; p<0.001) and Standardised Assessment of Concussion (SAC) (21.8% vs 7.5% CAN; p=0.008). Most prescribed physical rest (83.8% CAN, 75.5% US), while fewer recommended cognitive rest (47.5% CAN, 28.4% US; p=0.008). Return-to-play decisions were based primarily on clinical examination (89.1% US, 73.8% CAN; p=0.007); US physicians relied more on neurocognitive testing (29.7% vs 5.0% CAN; p<0.001) and recognised guidelines (63.4% vs 23.8% CAN; p<0.001). One-third of Canadian physicians received KTE from colleagues, websites and medical school training. Leading KTE preferences included Continuing Medical Education (CME) courses and online CME. CONCLUSIONS: Existing published recommendations regarding diagnosis/management of concussion are not always translated into practise, particularly the recommendation for cognitive rest; predicating enhanced, innovative CME initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".