A Retrospective Study of Patients with Minor Head Injury to Compare the Canadian CT Head Rule and the New Orleans Criteria
Bibliographic record
Abstract
Objective To compare the performance of the Canadian CT Head Rule (CCHR) and the New Orleans Criteria (NOC) in minor head injury patients. Method This retrospective cohort study collected data and CT head reports of all minor head injury patients from 1 January 2008 to 31 December 2010. We compared the sensitivity, specificity, positive and negative predictive values of both rules in predicting clinically important brain injury on CT and the need of neurosurgical intervention. Results We reviewed 474 patients with minor head injury. Seventy seven patients had clinically important brain injury and 11 underwent neurosurgical intervention. The sensitivity of the CCHR and NOC in predicting clinically important brain injury were 80% (95% confidence interval [CI] 70-88%) and 92% (95% CI 86-98%), respectively; and the specificity of the CCHR and NOC were 39% (95% CI 33-44%) and 17% (95% CI 13-21%), respectively. The sensitivity of the CCHR and NOC in predicting the need of neurosurgical intervention were 80% (95% CI 55-100%) and 100% (95% CI 100-100%), respectively; and the specificity of the CCHR and NOC were 36% (95% CI 31-41%) and 15% (95% CI 12-19%), respectively. The negative predictive values (NPV) of the CCHR and NOC for clinically important brain injury were 88% (95%CI 83-94%) and 91% (95%CI 84-98%); and for the need of neurosurgical intervention were 99% (95% CI 96-100%) and 100% (95% CI 100-100%). Amongst those missed cases, 88% in the CCHR group and 83% in the NOC group reported loss of consciousness. Conclusions The NOC is more sensitive but less specific than the CCHR in predicting both outcomes. Both rules have excellent NPV to rule out the need of neurosurgical intervention.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 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".