Retrospective Analysis of Emergency Computed Tomography Imaging Utilization at an Academic Centre: An Analysis of Clinical Indications and Outcomes
Bibliographic record
Abstract
PURPOSE: To explore resource utilization through evaluation of computed tomography (CT) imaging trends in the emergency department by examining common indications/outcomes for imaging in this setting. METHODS: A retrospective analysis of clinical indications/outcomes for all CT imaging in 3 emergency departments over a 1-year period was conducted. Scans were divided by body part and the most common indications for each type of scan were determined. Clinical outcomes from each study were extracted from final interpretations by the reporting radiologist. RESULTS: A total of 4556 CT scans were performed in the emergency department over a 1-year period. A total of 3.6% of all-comers to our emergency departments underwent CT scan as part of their investigation. There were 2107 head CTs (46%), 1296 (28%) abdominal CTs, 468 (10%) CTs of the chest, 408 (9%) CTs of the neck/spine, and 101 (2%) extremity CTs performed. The most common clinical indication for performing a CT head was focal neurological defect comprising 1534 (73%) of all CT heads. Twenty-four percent of abdominal CTs were for investigation of right lower quadrant pain, followed by flank pain (19%). Chest pain and shortness of breath were the most common indications for CTs of the chest (315 [75%]) with 10% of these examinations for this indication positive for pulmonary embolism. Trauma was the most common indication for neck CTs (296 [73%]) and extremities (70 [69%]). Nil acute was the most common final interpretation in all categories (79% CT heads, 75% neck CTs, 38% abdominal CTs, 43% chest CTs). CONCLUSIONS: Nil acute was the most common diagnosis; however, serious clinical outcomes were identified 40% of the time. Cross-sectional imaging remains an integral tool for triage and diagnosis in this environment as the cost of missing a diagnosis in this setting has a great impact on patient care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".