Trends in computed tomography utilisation in the emergency department: A 5 year experience in an urban medical centre in northern Taiwan
Bibliographic record
Abstract
BACKGROUND: Steady increase in computed tomography (CT) utilisation in the ED was observed in countries such as the USA, Canada, China and Korea; however, limited empirical data are available regarding Taiwan. OBJECTIVE: The objective of the present study is to quantify and compare trends in CT utilisation in the ED over a 5 year period in a medical centre in Taiwan. METHODS: Electronic chart review was performed in a medical centre with an annual ED census of 80 000 patients. Subjects >20 years of age who underwent CT scans during ED visits from 1 January 2005 to 31 December 2009 were identified. RESULTS: Among the 333 673 adult ED visits, 43 635 received CT scans, with a utilisation rate of 131 per 1000. Within the 5 year span, patient volume increased by 7.7%, whereas CT utilisation increased by 42.7%. The rates of increase in patient volume and CT utilisation were 5.0% and 32.4% in non-trauma; 19.7% and 97.8% in trauma. CT scans were mostly performed on the head (47%), abdomen (36%), followed by chest (10%) and miscellaneous (7%). An increase of 168% in spinal CTs for trauma patients was observed. An increase in CT utilisation was found in all age groups with a proportionate increase with increasing age in both trauma and non-trauma. CONCLUSION: ED CT utilisation has increased at a rate far exceeding the growth in ED patient volume. This may be attributed to the improved utility of CT in diagnosing serious pathology, more diagnostic indications for CT, ready availability and the necessity for diagnostic certainty in the ED.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".