Implementation of a guideline for computed tomography head imaging in head injury: A prospective study
Bibliographic record
Abstract
OBJECTIVE: To improve appropriate ordering of head computed tomography (CT) in patients presenting with a head injury by applying an evidence-based head injury guideline. METHODS: This was a comparison observational study of CT head ordering in the setting of head trauma between two groups of patients. There was a pre-guideline implementation group and a post-guideline implementation group. Our Southernhealth Head Injury Guideline was largely based on the Canadian CT Head Rule by Steill et al. 2001.We also applied the Canadian CT Head Rule to our post-guideline implementation group. RESULTS: CT ordering rate in the pre-guideline group was 31.6% compared with 59% in the post-guideline group with a relative risk of 1.88 (95% confidence interval [CI]: 1.56-2.27). Abnormal head CT were reported in 6.8% in the pre-guideline group and 5% in the post-guideline group (relative risk 0.88, 95% CI 0.44-1.51). When we applied the Canadian CT Head Rule to the prospective group, four patients with clinically significant abnormal head CT would not have been scanned. The sensitivity of the guideline was 100% (95% CI 79-100%), with a specificity of 43.22% (95% CI 37-48%) in diagnosing a significant head injury on CT. CONCLUSION: The Southernhealth Head Injury Guideline is safe and easy to apply to minor and major head injuries.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".