Persistently High Rates of Abdominal Computed Tomography Imaging Among Patients With Inflammatory Bowel Disease Who Present to the Emergency Department
Bibliographic record
Abstract
Abstract Background Recent guidelines recommended judicious use of abdominal computed tomography (CT) in the emergency department (ED) for inflammatory bowel disease. Trends in CT utilization over the last decade, including since the implementation of these guidelines, remain unknown. Methods We performed a single-centre, retrospective study between 2009 and 2018 to assess trends in CT utilization within 72 h of an ED encounter. Changes in the annual rates of CT imaging among adults with IBD were estimated by Poisson regression and CT findings by Cochran-Armitage or Cochran-Mantel Haenszel tests. Results A total of 3000 abdominal CT studies were performed among 14,783 ED encounters. CT utilization increased annually by 2.7% in Crohn’s disease (CD) (95% confidence interval [CI], 1.2 to 4.3; P = 0.0004), 4.2% in ulcerative colitis (UC) (95% CI, 1.7 to 6.7; P = 0.0009) and 6.3% in IBD unclassifiable (95% CI, 2.5 to 10.0; P = 0.0011). Among encounters with gastrointestinal symptoms, 60% with CD and 33% with UC underwent CT imaging in the final year of the study. Urgent CT findings (obstruction, phlegmon, abscess or perforation) and urgent penetrating findings alone (phlegmon, abscess or perforation) comprised 34% and 11% of CD findings, and 25% and 6% of UC findings, respectively. The CT findings remained stable overtime for both CD (P = 0.13) and UC (P = 0.17). Conclusion Our study demonstrated persistently high rates of CT utilization among patients with IBD who presented to the ED over the last decade. Approximately one third of scans demonstrated urgent findings, with a minority demonstrating urgent penetrating findings. Future studies should aim to identify patients in whom CT imaging is most appropriate.
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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 | 0.004 |
| 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 | 0.001 |
| 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".