Evaluation of Clinical Characteristics and CT Decision Rules in Elderly Patients with Minor Head Injury: A Prospective Multicenter Cohort Study
Bibliographic record
Abstract
Age is variably described as a minor or major risk factor for traumatic intracranial lesions after head injury. However, at present, no specific CT decision rule is available for elderly patients with minor head injury (MHI). The aims of this prospective multicenter cohort study were to assess the performance of existing CT decision rules for elderly MHI patients and to compare the clinical and CT characteristics of elderly patients with the younger MHI population. Thirty-day mortality between two age groups (cutoff ≥ 60 years), along with clinical and CT characteristics, was evaluated with four CT decision rules: the National Institute for Health and Care Excellence (NICE) guideline, the Canadian CT Head Rule (CCHR), the New Orleans Criteria (NOC), and the CT Head Injury Patients (CHIP) rule. Of the 5517 MHI patients included, 2310 were aged ≥ 60 years. Elderly patients experienced loss of consciousness (17% vs. 32%) and posttraumatic amnesia (23% vs. 31%) less often, but intracranial lesions (13% vs. 10%), neurological deterioration (1.8% vs. 0.2%), and 30-day mortality (2.0% vs. 0.1%) were more frequent than in younger patients (all p < 0.001). Elderly patients with age as their only risk factor showed intracranial lesions in 5% (NOC and CHIP) to 8% (CCHR and NICE) of cases. The sensitivity of decision rules in the elderly patients was 60% (CCHR) to 97% (NOC) when age was excluded as a risk factor. Current risk factors considered when evaluating elderly patients show lower sensitivity to identify intracranial abnormalities, despite more frequent intracranial lesions. Until age-specific CT decision rules are developed, it is advisable to scan every elderly patient with an MHI.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".