Implementation of the Canadian C-Spine Rule: prospective 12 centre cluster randomised trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of an active strategy to implement the validated Canadian C-Spine Rule into multiple emergency departments. DESIGN: Matched pair cluster randomised trial. SETTING: University and community emergency departments in Canada. Participants 11 824 alert and stable adults presenting with blunt trauma to the head or neck at one of 12 hospitals. INTERVENTIONS: Six hospitals were randomly allocated to the intervention and six to the control. At the intervention sites, active strategies were used to implement the Canadian C-Spine Rule, including education, policy, and real time reminders on radiology requisitions. No specific intervention was introduced to alter the behaviour of doctors requesting cervical spine imaging at the control sites. MAIN OUTCOME MEASURE: Diagnostic imaging rate of the cervical spine during two 12 month before and after periods. RESULTS: Patients were balanced between control and intervention sites. From the before to the after periods, the intervention group showed a relative reduction in cervical spine imaging of 12.8% (95% confidence interval 9% to 16%; 61.7% v 53.3%; P=0.01) and the control group a relative increase of 12.5% (7% to 18%; 52.8% v 58.9%; P=0.03). These changes were significant when both groups were compared (P<0.001). No fractures were missed and no adverse outcomes occurred. CONCLUSIONS: Implementation of the Canadian C-Spine Rule led to a significant decrease in imaging without injuries being missed or patient morbidity. Final imaging rates were much lower at intervention sites than at most US hospitals. Widespread implementation of this rule could lead to reduced healthcare costs and more efficient patient flow in busy emergency departments worldwide. TRIAL REGISTRATION: Clinical trials NCT00290875.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".