Computed tomography of the head for adult patients with minor head injury: are clinical decision rules a necessary evil?
Bibliographic record
Abstract
INTRODUCTION: This study aimed to evaluate compliance with and performance of the Canadian Computed Tomography Head Rule (CCHR), and its applicability to the Singapore adult population with minor head injury. METHODS: We conducted a retrospective study over six months of consecutive patients who presented to the adult emergency department (ED) with minor head injury. Data on predictor variables indicated in the CCHR was collected and compliance with the CCHR was assessed by comparing the recommendations for head computed tomography (CT) to its actual usage. RESULTS: In total, 349 patients satisfied the inclusion criteria. Common mechanisms of injury were falls (59.3%), motor vehicle crashes (16.9%) and assault (12.0%). 249 (71.3%) patients underwent head CT, yielding 42 (12.0%) clinically significant findings. 1 (0.3%) patient required neurosurgical intervention. According to the CCHR, head CT was recommended for 209 (59.9%) patients. Compliance with the CCHR was 71.3%. Among the noncompliant group, head CT was overperformed for 20.1% and underperformed for 8.6% of patients. Multivariate logistic regression analysis revealed that absence of retrograde amnesia (odds ratio [OR] 4.1, 95% confidence interval [CI] 1.8-9.7) was associated with noncompliance to the CCHR. Factors associated with underperformance were absence of motor vehicle crashes as a mechanism of injury (OR 6.6, 95% CI 1.2-36.3) and absence of headache (OR 10.8, 95% CI 1.3-87.4). CONCLUSION: Compliance with the CCHR for adult patients with minor head injury remains low in the ED. A qualitative review of physicians' practices and patients' preferences may be carried out to evaluate reasons for noncompliance.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".