Canadian CT head rule and New Orleans Criteria in mild traumatic brain injury: comparison at a tertiary referral hospital in Japan
Bibliographic record
Abstract
We compared Canadian computed tomography (CT) head rule (CCHR) and New Orleans Criteria (NOC) in predicting important CT findings in patients with mild traumatic brain injury (TBI). We included 142 consecutive patients with mild TBI [Glasgow coma scale (GCS) 13-15] who showed at least one of the risk factors stated in the CCHR or the NOC. We introduced two scores: a Canadian from the CCHR and a New Orleans from the NOC. A patient's score represented a sum of the number of positive items. We examined the relationship between scores or items and the presence of important CT findings. Only the Canadian was significantly associated with important CT findings in multivariate analyses and showed higher area under the receiver operating characteristic curve (AUC) either in all 142 patients (GCS 13-15: P = 0.0130; AUC = 0.69) or in the 67 with a GCS = 15 (P = 0.0128, AUC = 0.73). Of items, ">60 years" or "≥65 years" included in either guideline was the strongest predictor of important CT finding, followed by "GCS < 15 after 2 h" included only in the CCHR. In a tertiary referral hospital in Japan, CCHR had higher performance than the NOC in predicting important CT findings.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".