Evaluation of Cranial Computed Tomography use and Guideline Compliance in Head Trauma Patients Presenting to the Emergency Department
Bibliographic record
Abstract
BACKGROUND: Head trauma was the significant public health issue and a common cause of emergency department visits. Cranial computed tomography (CT) was widely used in its evaluation; however, overuse-particularly in mild cases-raises concerns about patient safety and healthcare efficiency. AIM: This study aimed to evaluate the appropriateness of cranial CT use in head trauma patients, its relationship with Glasgow Coma Scale (GCS) scores, and the prevalence of potentially avoidable imaging. METHODS: This retrospective study included 1,000 patients presenting with head trauma, who underwent cranial CT. Data collected included demographics, trauma mechanism, GCS score, CT findings, and indication for imaging. CT necessity was assessed using the Canadian CT Head Rule and New Orleans Criteria. CTs performed in patients with GCS 13-15, normal findings, and no guideline-based indications were classified as potentially avoidable. RESULTS: Of all patients, 65% were male, with a mean age of 42.1 ± 20.7 years. Mild trauma (GCS 13-15) was present in 77.5% of cases. Intracranial pathology was detected in 35.9% overall, with higher rates in patients with moderate and severe trauma. Unnecessary CT imaging was found in 57% of all cases, and in 80.2% of mild trauma cases. A statistically significant association was found between lower GCS scores and intracranial findings (P < 0.001). CONCLUSION: Cranial CT was often overused in mild head trauma without adherence to clinical guidelines. Promoting the use of decision support tools and raising awareness among clinicians and patients are crucial for reducing potentially avoidable imaging, radiation exposure, and healthcare burden.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".