Canadian Computed Tomography Head Rule and Its Impact on Singaporean Practice
Bibliographic record
Abstract
Introduction We aim to determine the usefulness of the Canadian Computed Tomography (CT) Head Rule in patients who present with minor and minimal head injury to the emergency department. Methods Clinical information was retrospectively collected and the need for CT head was evaluated. The primary outcome measure was the requirement for neurological intervention. The secondary outcome measure was brain injury requiring admission or neurological follow-up. Results A total of 1127 cases were reviewed. About 6.3% had clinically important brain injury; and 1.2% required neurological intervention. The high-risk factors were 45.2% sensitive (95% confidence interval [CI]=27.8-63.7%) and 76.2% specific (95% CI=73.5-78.7%) for predicting need for neurological intervention. All seven risk factors were 73.2% sensitive (95% CI=61.2-82.7%) and 69.8% specific (95% CI=66.9-72.5%) for predicting clinically important brain injury. Overall, the rule had a positive predictive value of 14.0 (95% CI=10.7-18.1) and negative predictive value of 97.5 (95% CI=96.0-98.4). Conclusions In this retrospective validation of the Canadian CT Head Rule in the Singaporean context, we conclude that the lower sensitivity of the rule could be attributed to local neurosurgical practice, lack of application of the rule among clinicians and inclusion of patients with minimal head injury. Practically, the high NPV will mean that patients who do not fulfill the rule can be safely discharged with head injury advice, without the need for a scan. The judicious use of CT head can achieve savings for our health-care system. (Hong Kong j.emerg.med. 2015;22:359-363)
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".