3.1 Survey of emergency physician concussion management patterns in southwestern Ontario
Bibliographic record
Abstract
Objective We aim to clarify practice patterns of concussion assessment and management at a large tertiary care centre and regional referring hospitals in Southwestern Ontario, Canada to determine the proportion using evidence- based concussion management. Design Survey. Setting An academic hospital in London, Ontario, Canada and surrounding community hospitals. Participants Emergency Medicine Physicians. 75% work in academic settings and 25% in community Emergency Departments. Outcome Measures The survey asked whether respondents were using validated symptoms assessment scales in patients with suspected concussion for assessment and risk stratification for post concussion syndrome. We also asked whether physicians provide verbal and written discharge instructions and what the instructions include. Main Results When assessing patients with suspected concussion, 69% of respondents do not routinely use a symptom assessment scale and 31% use part or all of the SCAT-5 questionnaire.19% report not considering risk of post concussion syndrome, whereas 71% report using patient factors such as history of previous concussion or mental health disorders. At time of discharge, 81% of respondents reported giving verbal instructions and 88% give written instructions. 90% give institutional handouts with discharge instructions, while 19% use the SCAT-5 handout and 9% use the Parachute Canada Handout. Conclusions In Southwestern Ontario, there is variation in the use of validated concussion tools for the diagnosis of concussion as well as for use in risk stratifying patients for post-concussive syndrome. There could be benefit to standardization of concussion management practices, facilitated by a unified concussion management guideline targeting Canadian providers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".