A129 RATES AND CLINICAL PREDICTORS OF URGENT FINDINGS ON ABDOMINOPELVIC COMPUTED TOMOGRAPHY IN EMERGENCY DEPARTMENT PATIENTS WITH INFLAMMATORY BOWEL DISEASE
Bibliographic record
Abstract
Abdominopelvic computed tomography (APCT) has become a common imaging modality to investigate abdominal complaints, particularly in the emergency department (ED). Despite their clinical utility, APCTs carry a risk of cumulative radiation exposure. This risk is of particular concern in inflammatory bowel disease (IBD) patients given the chronic relapsing nature of the disease and repeated APCT scans. Therefore, attempts must be made to minimize the number of APCT scans in IBD patients by identifying those most likely to have urgent findings. We aimed to quantify the rates of abdominopelvic computed tomography (APCT) in IBD patients presenting to the ED with gastrointestinal complaints and to examine clinical predictors of urgent findings in this population. A retrospective cross-sectional study was performed among patients with IBD presenting to 3 EDs in Saskatoon between 2014–2015 with a gastrointestinal complaint. The primary outcomes were the rate of APCT, and the rates of obstruction, perforation, abscess or non-IBD related urgent findings (OPAN) on APCT. Clinical predictive variables analyzed were demographics, clinical symptoms, IBD medication use, physical exam findings, and laboratory values. Variables with a univariate P<0.2 were included in a multivariable logistic regression model. 181 patients presenting over 265 ED encounters met the inclusion criteria. 92 patients (50.8%) received a total of 104 APCTs. 40 patients had ulcerative colitis (UC). 11 (27.5%) of the UC patients received APCT. Only 1 of 12 APCTs in UC identified OPAN (8.3%) - a duodenal ulcer. 141 patients had Crohn’s disease. 81 (57.4%) of the CD patients received APCT. 36 of 92 APCTs in CD identified OPAN (39.1%). The CT findings regarding OPAN for CD patients are shown in Table 1. Clinical variables retained in the final model were leukocyte count >11, diarrhea and BRBPR/hematochezia. 39.1% of the APCTs performed in the ED among patients with CD showed urgent findings. Only 1 APCT performed on UC patients found non-IBD related urgent findings. To reduce unnecessary radiation exposure, the selection process for IBD patients referred for APCT must be improved. Using a clinical predictive model including leukocyte count >11, presence of diarrhea and presence of BRBPR/hematochezia may help in the selection of CD patients who should be investigated with APCT. Frequency Distribution of OPAN Pathology on CT (N=92) Subjects may have multiple types of pathology. None
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".