Accuracy of Canadian CT Head Rule and New Orleans Criteria for Minor Head Trauma; a Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
INTRODUCTION: The present meta-analysis has two objectives; primarily, the predictive values of Canadian computed tomography (CT) head rule (CCHR) and New Orleans Criteria (NOC) will be compared. Secondly, the possibility of interchangeable use of the two models in cases of contraindication will be evaluated. METHODS: " command. RESULTS: Data from 14 articles were included (21140 samples). Summary sensitivity, specificity, and diagnostic odds ratio of CCHR in prediction of positive CT findings were 89.8% (95% CI: 79.6 to 95.2), 38.3% (95% CI: 34.0 to 42.8), and 5.5 (95% CI: 2.3 to 13.1), respectively. In addition, summary sensitivity, specificity, and diagnostic odds ratio of NOC in prediction of positive CT findings were 97.2% (95% CI: 89.7 to 99.2), 12.3% (95% CI: 7.4 to 19.8), and 4.8 (95% CI: 1.2 to 18.3), respectively. Summary sensitivity, specificity, and diagnostic odds ratio of CCHR in prediction of clinically important TBI (ciTBI) in mild TBI patients were 92.5% (95% CI: 79.5 to 97.5), 40.1% (95% CI: 34.8 to 45.6), and 8.3 (95% CI: 2.4 to 29.2), respectively. In addition, summary sensitivity, specificity, and diagnostic odds ratio of NOC in prediction of ciTBI were 98.3% (95% CI: 93.8 to 99.6), 8.5% (95% CI: 4.8 to 14.5), and 5.4 (95% CI: 1.5 to 20.0), respectively. CONCLUSION: The present meta-analysis demonstrated that both CCHR and NOC scores have a good predictive value in predicting the presence of abnormal findings in CT scan and ciTBI. The similar performance of CCHR and NOC models results in their interchangeable use in cases of contraindication.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.036 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".