International Survey of Emergency Physicians’ Awareness and Use of the Canadian Cervical‐Spine Rule and the Canadian Computed Tomography Head Rule
Bibliographic record
Abstract
OBJECTIVES: The derivation and validation studies for the Canadian Cervical-Spine (C-Spine) Rule (CCR) and the Canadian Computed Tomography (CT) Head Rule (CCHR) have been published in major medical journals. The objectives were to determine: 1) physician awareness and use of these rules in Australasia, Canada, the United Kingdom, and the United States and 2) physician characteristics associated with awareness and use. METHODS: A self-administered e-mail and postal survey was sent to members of four national emergency physician (EP) associations using a modified Dillman technique. Results were analyzed using repeated-measures logistic regression models. RESULTS: The response rate was 54.8% (1,150/2,100). Reported awareness of the CCR ranged from 97% (Canada) to 65% (United States); for the CCHR it ranged from 86% (Canada) to 31% (United States). Reported use of the CCR ranged from 73% (Canada) to 30% (United States); for the CCHR, it was 57% (Canada) to 12% (United States). Predictors of awareness were country, type of rule, full-time employment, younger age, and teaching hospital (p < 0.05). Significant differences in use of the CCR by country were observed, but not for the CCHR. Teaching hospitals were more likely to use the CCR than nonteaching hospitals, but less likely to use the CCHR. CONCLUSIONS: This large international study found notable differences among countries with regard to knowledge and use of the CCR and CCHR. Awareness and use of both rules were highest in Canada and lowest in the United States. While younger physicians, those employed full-time, and those working in teaching hospitals were more likely to be aware of a decision rule, age and employment status were not significant predictors of use. A better understanding of factors related to awareness and use of emergency medicine (EM) decision rules will enhance our understanding of knowledge translation and facilitate strategies to enhance dissemination and implementation of future rules.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".