Canadian computed tomography head rule and New Orleans criteria in mild traumatic brain injury: Comparison at an urban tertiary care facility in Pakistan
Bibliographic record
Abstract
Background: Traumatic brain injury (TBI) is a leading cause of mortality and morbidity worldwide, with road traffic accidents being the predominant cause in Pakistan. Computed tomography (CT) scans have become the cornerstone of investigation for all TBIs, but their widespread use raises concerns about cost-effectiveness, radiation exposure, and incidental findings. This study aimed to validate the applicability of the Canadian CT head rule (CCHR) and New Orleans Criteria (NOC) in the Pakistani population and compare their sensitivity and specificity. Methods: A cross-sectional study was conducted in a tertiary care academic hospital in Pakistan, including consecutive patients with acute, mild brain injury. The primary outcome was "clinically important brain injury," while the secondary outcome was "need for neurosurgical intervention." Univariate analysis using Chi square was performed for each variable to assess association with CT findings. Sensitivity, specificity, and accuracy were calculated to evaluate the performance of each decision rule. Results: Most of the patients in our study had a Glasgow Coma Scale (GCS) score of 15 (92.6%). Headache was the most common parameter overall (61.7%). Clinically important CT was detected in 68 (6.7%) patients. Only 1 of the NOC and 4 CCHR variables demonstrated statistically significant association with clinically significant CT. The CCHR was 64% sensitive for detecting clinically important CTs in trauma patients with GCS of 13-15, and the NOC was 86% sensitive, with respective specificities of 70% and 33%. For predicting the need for neurosurgical intervention, the sensitivities of CCHR and NOC were 61% and 85%, and specificity was 68% and 32%, respectively. Conclusion: We concluded that the CCHR was more specific and accurate, and it has the potential to have a greater influence on CT ordering rates than the NOC. Further studies are recommended to validate the tools for the Pakistani population.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".