Evaluation of Canadian CT head injury rule performance among patients with seizure- and non-seizure-related head injuries: A pilot study
Bibliographic record
Abstract
INTRODUCTION: Canadian CT Head Injury Rule (CTHR) is used to evaluate the necessity of a computed tomography (CT) scan in patients with TBI suspicion. Despite known negative effects of epilepsy on brain structural integrity, patients with SRHI are still assessed with CTHR. OBJECTIVES: Assess the prognostic efficiency of CTHR in seizure-related and non-seizure-related head injury. Secondarily, identify CTHR criteria significantly associated with acute CT findings in SRHI patients. METHODS: Data on 124 patients with SRHIs were extracted from the medical records followed by 169 non-SRHI admissions. Head injuries prior to seizures, age below 16 years-old, patients on blood thinners, and lack of CT imaging were considered exclusion criteria. Propensity score matching equalized both cohorts at 118 cases. Sensitivity, specificity, and the associated likelihood ratios were calculated. CTHR criteria were analysed to identify TBI predictors in SRHIs. RESULTS: Significantly greater number of non-SRHI patients had positive CTHR classification compared to SRHI. No significant difference was found in the number of positive CT classifications. In the non-SHRI group, CTHR was associated with a LR+ of 1.29 and a LR- of 0. In contrast, within the SHRI group, CTHR demonstrated an LR+ of 1.72 and an LR- of 0.34. GCS < 15 and signs of basilar skull fracture were significantly correlated with positive CT findings in this cohort. CONCLUSION: Our findings emphasize the need for tailored tools for managing SRHIs. Future research should focus on the development of more sensitive and specific guidelines by exploring factors correlated with acute head 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".