A prospective cluster-randomized trial to implement the Canadian CT Head Rule in emergency departments
Bibliographic record
Abstract
BACKGROUND: The Canadian CT Head Rule was developed to allow physicians to be more selective when ordering computed tomography (CT) imaging for patients with minor head injury. We sought to evaluate the effectiveness of implementing this validated decision rule at multiple emergency departments. METHODS: We conducted a matched-pair cluster-randomized trial that compared the outcomes of 4531 patients with minor head injury during two 12-month periods (before and after) at hospital emergency departments in Canada, six of which were randomly allocated as intervention sites and six as control sites. At the intervention sites, active strategies, including education, changes to policy and real-time reminders on radiologic requisitions were used to implement the Canadian CT Head Rule. The main outcome measure was referral for CT scan of the head. RESULTS: Baseline characteristics of patients were similar when comparing control to intervention sites. At the intervention sites, the proportion of patients referred for CT imaging increased from the "before" period (62.8%) to the "after" period (76.2%) (difference +13.3%, 95% CI 9.7%-17.0%). At the control sites, the proportion of CT imaging usage also increased, from 67.5% to 74.1% (difference +6.7%, 95% CI 2.6%-10.8%). The change in mean imaging rates from the "before" period to the "after" period for intervention versus control hospitals was not significant (p = 0.16). There were no missed brain injuries or adverse outcomes. INTERPRETATION: Our knowledge-translation-based trial of the Canadian CT Head Rule did not reduce rates of CT imaging in Canadian emergency departments. Future studies should identify strategies to deal with barriers to implementation of this decision rule and explore more effective approaches to knowledge translation. (ClinicalTrials.gov trial register no. NCT00993252).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".