Risk factors indicating the need for cranial CT scans in elderly patients with head trauma: an Austrian trial and comparison with the Canadian CT Head Rule
Bibliographic record
Abstract
OBJECT: This study presents newly defined risk factors for detecting clinically important brain injury requiring neurosurgical intervention and intensive care, and compares it with the Canadian CT Head Rule (CCHR). METHODS: This prospective cohort study was conducted in a single Austrian Level-I trauma center and enrolled a consecutive sample of mildly head-injured adults who presented to the emergency department with witnessed loss of consciousness, disorientation, or amnesia, and a Glasgow Coma Scale (GCS) score of 13-15. The studied population consisted of a large number of elderly patients living in Vienna. The aim of the study was to investigate risk factors that help to predict the need for immediate cranial CT in patients with mild head trauma. RESULTS: Among the 12,786 enrolled patients, 1307 received a cranial CT scan. Four hundred eighty-nine patients (37.4%) with a mean age of 63.9 ± 22.8 years had evidence of an acute traumatic intracranial lesion on CT. Three patients (< 0.1%) were admitted to the intensive care unit for neurological observation and received oropharyngeal intubation. Seventeen patients (0.1%) underwent neurosurgical intervention. In 818 patients (62.6%), no evidence of an acute trauma-related lesion was found on CT. Data analysis showed that the presence of at least 1 of the following factors can predict the necessity of cranial CT: amnesia, GCS score, age > 65 years, loss of consciousness, nausea or vomiting, hypocoagulation, dementia or a history of ischemic stroke, anisocoria, skull fracture, and development of a focal neurological deficit. Patients requiring neurosurgical intervention were detected with a sensitivity of 90% and a specificity of 67% by using the authors' analysis. In contrast, the use of the CCHR in these patients detected the need for neurosurgical intervention with a sensitivity of only 80% and a specificity of 72%. CONCLUSIONS: The use of the suggested parameters proved to be superior in the detection of high-risk patients who sustained a mild head trauma compared with the CCHR rules. Further validation of these results in a multicenter setting is needed. Clinical trial registration no.: NCT00451789 ( ClinicalTrials.gov .).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".