Utilization of Diagnostic Imaging and Ionization Radiation Exposure Among an Inflammatory Bowel Disease Inception Cohort
Bibliographic record
Abstract
BACKGROUND: Abdominal imaging is important in managing inflammatory bowel disease (IBD). We characterized utilization of imaging and exposure to ionizing radiation. METHODS: We enumerated abdominal diagnostic imaging in a population-based cohort of IBD patients between 1994 and 2016. Trends in utilization of abdominal computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound were characterized. Cumulative doses of ionizing radiation were compared between IBD patients and non-IBD controls and between Crohn's disease (CD) and ulcerative colitis (UC) patients. Regression models were constructed to assess predictors of high ionizing radiation exposure. RESULTS: There were 72,933 incident cases of IBD. During the first 5 years of diagnosis, IBD patients were exposed to nearly 6-fold higher exposure to cumulative ionizing radiation attributable to abdominal imaging compared with non-IBD controls (18.6 mSv vs 2.9 mSv). Cumulative ionizing radiation exposure was higher in CD than UC (26.0 mSv vs 12.0 mSv; P < 0.001). Crohn's disease patients were more than twice as likely as UC patients to exceed 50 mSv (15.6% vs 6.2%; P < 0.001) and 100 mSV (5.0% vs 2.1%; P < 0.001). There was geographic variation in ionizing radiation exposure, and individuals of lower income were more likely to have high exposure. Utilization of abdominal MRI has increased substantially, peaking between 2007 and 2012 and increasing annually at 34%, which coincided with an annual 2% decline in the use of abdominal CT. CONCLUSIONS: Crohn's disease patients are at highest risk for high exposure to ionizing radiation, with a subgroup receiving potentially harmful levels. Increasing utilization and access to abdominal MRI may alleviate exposure.
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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 | 0.001 |
| 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 | 0.000 | 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".