Predictive factors associated with immunosuppressive agent use in ulcerative colitis: a case–control study
Bibliographic record
Abstract
BACKGROUND: Some patients with ulcerative colitis (UC) require immunosuppressants as maintenance therapy. AIM: To assess epidemiological, clinical and disease factors at diagnosis that predict immunosuppressant use in UC. METHODS: All UC patients diagnosed between 1992 and 2005 and currently managed in the inflammatory bowel disease (IBD) clinic were included. Forty-three patients who currently or previously received azathioprine (AZA) or mercaptopurine (MP) for UC were compared with 130 controls. Charts were reviewed and logistic regression analyses were applied to identify factors associated with AZA or MP use. RESULTS: In univariate model, seven factors at diagnosis correlated with AZA use: male gender [odds ratio (OR) 2.2]; left-sided or extensive colitis or pancolitis (OR 8.7-14.1); systemic steroid use within the first 6 months of diagnosis (OR 5.1); more than 10 bowel movements daily (OR 6.4); persistent or mostly blood in stool (OR 2.8); endoscopic proven moderate to severe disease (OR 7.2-12.0) and requirement of hospitalization (OR 2.7) on diagnosis. In multivariate model, the first three factors were shown to be statistically significant. CONCLUSION: Male gender, initial presentation with severe and extensive disease clinically and endoscopically, requirement of hospitalization on diagnosis or systemic steroids within 6 months of diagnosis are predictive factors for immunosuppressant use in UC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".