Perceived value of computed tomography imaging for patients with inflammatory bowel disease in the emergency department: a Canadian survey
Bibliographic record
Abstract
Background: There are high rates of computed tomography (CT) utilization in the emergency department (ED) for patients with inflammatory bowel disease (IBD), despite guidelines recommending judicious use. We performed a national survey to better understand perceptions and practice patterns of Canadian physicians related to CT imaging in the ED. Methods: Our survey was developed by a multistep iterative process with input from key stakeholders between 2021 and 2022. It evaluated Canadian gastroenterologists', surgeons', and emergency physicians' (1) perceived rates of IBD findings detected by CT, (2) likelihood of performing CT for specific presentations and (3) comfort in diagnosing IBD phenotypes/complications without CT. Results: A total of 208 physicians responded to our survey: median age 44 years (IQR, 37-50), 63% male, 68% academic, 44% emergency physicians, 39% gastroenterologists, and 17% surgeons. Compared with emergency physicians and surgeons, gastroenterologists more often perceived that CT would detect inflammation alone and less often IBD complications. Based on established rates in the literature, 13 (16%) gastroenterologists, 33 (40%) emergency physicians, and 21 (60%) surgeons overestimated the rates of at least one IBD complication. Although most physicians were more comfortable diagnosing inflammation compared to IBD complications without CT, gastroenterologists were significantly less likely to recommend CT imaging for non-obstructive/penetrating presentations compared with emergency physicians and surgeons with results that varied by IBD subtype. Conclusion: This national survey demonstrates differences in physician perceptions and practices regarding CT utilization in the ED and can be used as a framework for educational initiatives regarding appropriate usage of this modality.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".