Sport concussion knowledge base, clinical practices, and needs for continuing medical education: a survey of family physicians and cross-border comparison
Bibliographic record
Abstract
Objective To identify sport concussion knowledge base, practice patterns and current/preferred methods of Knowledge Transfer and Exchange (KTE) in two distinct populations of family physicians. Design Cross-sectional study, using a survey design. Setting Alberta, Canada (CAN); North/South Dakota, USA (US). Rural (64.4% US, 27.5% CAN; p=<0.001); walk-in/acute care (28.8% CAN, 12.9% US; p=0.008). Participants Recruitment: CAN physicians by mail: 80/3154 responses (2.5%); US physicians: American Academy of Family Physicians database: 109/545 responses (20%). Intervention/Instrument On-line survey questionnaire. Outcome Measures Relative percentages diagnosing/treating concussions; comparison of management strategies (including return-to-play), and current/preferred KTE. Results Etiologies: Sports/recreation (52.5% CAN); organised sports (76.5% US). Tools: Clinical examination (93.8% CAN, 88.1% US); Sport Concussion Assessment Tool (SCAT/SCAT2) (33.8% CAN, 26.7% US); balance testing (25.0% CAN, 26.7% US); concussion grading scales (26.7% US, 8.8% CAN, p=0.002); computerised neurocognitive testing (19.8% US, 1.3% CAN; p≤0.001); Standardised Assessment of Concussion (21.8% US, 7.5% CAN; p=0.008). Treatment: Physical rest (83.8% CAN, 75.5% US); cognitive rest (47.5% CAN, 28.4% US; p=0.008). Return-to-play: Clinical examination (89.1% US, 73.8% CAN; p=0.007); neurocognitive testing (29.7% US, 5.0% CAN; p≤0.001); guidelines (63.4% US, 23.8% CAN; p≤0.001). KTE sources: Colleagues (31.3% CAN, 8.8% US; p≤0.001), websites (27.5% CAN, 15.7% US; p=0.052); medical school (35.0% CAN, 12.7% US; p≤0.001). KTE Preferences: Continuing Medical Education (CME) courses (65.0% CAN, 37.3% US; p≤0.001), and online CME (47.5% Can, 29.4% US; p=0.012). Conclusions Despite evolution of concussion diagnosis/management guidelines, significant knowledge gaps exist between evidence-based recommendations clinical practice patterns. This predicates enhanced and innovative CME initiatives for KTE. Competing interests None.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".