Prevalence of and Outcomes Associated with Corticosteroid Prescription in Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Corticosteroids are widely used in the management of inflammatory bowel disease (IBD) and are associated with significant side effects. The real world effectiveness of newer drug therapies at reducing corticosteroid use is yet to be reported. The overall burden of corticosteroid use is poorly characterized. METHODS: We used a population-based IBD database to evaluate the overall prevalence of corticosteroid exposure, corticosteroid-free survival, and heavy corticosteroid use (≥3000 mg of prednisone or equivalent in a 365-day period). Regression models were used to assess predictors of heavy corticosteroid use and the relationship between corticosteroid dose in the first year after diagnosis and the need for continued corticosteroid use and surgery. RESULTS: The proportion of persons with IBD prescribed corticosteroids within 1, 5, and 10 years of diagnosis was 35.2%, 52.0%, and 62.8%, respectively. Persons with ulcerative colitis, males, and diagnosis before age 25 were more likely to use corticosteroids and have higher cumulative exposure. Heavy corticosteroid use in the first year after IBD diagnosis was associated with a 3 times increased hazard of resective surgery. Cumulative corticosteroid exposure did not decrease among those diagnosed with IBD in more recent years, despite increasing use of immunomodulators. CONCLUSIONS: A plurality of IBD patients will be exposed to corticosteroids over the course of disease, mostly in the first year. Heavy corticosteroid use in the first year of IBD is a strong predictor of subsequent surgery. Cumulative exposure to corticosteroids use is not decreasing despite increasing uptake of immunomodulators.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".