The application of North American CT scan criteria to an Australian population with minor head injury
Bibliographic record
Abstract
OBJECTIVE: To determine potential changes in the number of CT head scans performed if the New Orleans Criteria (NOC) or Canadian CT Head Rule (CCTR) was applied to an Australian emergency department population of minor head injured (MHI) patients. METHODS: A retrospective chart review was conducted in an adult metropolitan teaching hospital in Brisbane. All patients presenting over a 3-month period with a GCS Score of 15 following an MHI and had a CT head scan performed were selected for analysis. Using clinically significant CT abnormalities and neurological intervention as the outcome measures, the NOC and CCTR were applied to determine if CT scanning was considered necessary. RESULTS: Of the 240 patients reviewed, 230 had a normal CT scan and 10 had clinically significant CT abnormalities. One patient with CT abnormality required neurosurgical intervention. Application of the NOC would have resulted in a 3.8% (95% CI 1.7-7.0%) reduction in CT scans performed without missing any patients with CT abnormalities or requiring neurological intervention. Application of the CCTR using both high and low risk factors would have resulted in a 46.7% (95% CI 40.2-53.2%) reduction in CT scans performed without missing the patient requiring neurological intervention, but would not have detected two patients with clinically significant CT abnormalities. CONCLUSION: Neither the NOC nor the CCRT appear suitable for significantly reducing the number of normal CT head scans performed without missing clinically significant CT abnormalities when applied to our current clinical practice.
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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 | 0.002 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".