Comparison of the Canadian CT head rule and the new orleans criteria in patients with minor head injury
Bibliographic record
Abstract
AIM: The aim of the study was to compare the New Orleans Criteria and the New Orleans Criteria according to their diagnostic performance in patients with mild head injury. METHODS: The study was designed and conducted prospectively after obtaining ethics committee approval. Data was collected prospectively for patients presenting to the ED with Minor Head Injury. After clinical assessment, a standard CT scan of the head was performed in patients having at least one of the risk factors stated in one of the two clinical decision rules. Patients with positive traumatic head injury according to BT results defined as Group 1 and those who had no intracranial injury defined as Group 2. Statistical analysis was performed with SPSS 11.00 for Windows. ROC analyze was performed to determine the effectiveness of detecting intracranial injury with both decision rules. p < 0.05 was considered statistically significant. RESULTS: 175 patients enrolled the study. Male to female ratio was 1.5. The mean age of the patients was 45 ± 21,3 in group 1 and 49 ± 20,6 in group 2. The most common mechanism of trauma was falling. The sensitivity and specificity of CCHR were respectively 76.4% and 41.7%, whereas sensitivity and specificity of NOC were 88.2% and 6.9%. CONCLUSION: The CCHR has higher specificity, PPV and NPV for important clinical outcomes than does the NOC.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".