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 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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".