Perianal disease, small bowel disease, smoking, prior steroid or early azathioprine/biological therapy are predictors of disease behavior change in patients with Crohn’s disease
Bibliographic record
Abstract
AIM: To assess the combined effect of disease phenotype, smoking and medical therapy [steroid, azathioprine (AZA), AZA/biological therapy] on the probability of disease behavior change in a Caucasian cohort of patients with Crohn's disease (CD). METHODS: Three hundred and forty well-characterized, unrelated, consecutive CD patients were analyzed (M/F: 155/185, duration: 9.4 +/- 7.5 years) with a complete clinical follow-up. Medical records including disease phenotype according to the Montreal classification, extraintestinal manifestations, use of medications and surgical events were analyzed retrospectively. Patients were interviewed on their smoking habits at the time of diagnosis and during the regular follow-up visits. RESULTS: A change in disease behavior was observed in 30.8% of patients with an initially non-stricturing, non-penetrating disease behavior after a mean disease duration of 9.0 +/- 7.2 years. In a logistic regression analysis corrected for disease duration, perianal disease, smoking, steroid use, early AZA or AZA/biological therapy use were independent predictors of disease behavior change. In a subsequent Kaplan-Meier survival analysis and a proportional Cox regression analysis, disease location (P = 0.001), presence of perianal disease (P < 0.001), prior steroid use (P = 0.006), early AZA (P = 0.005) or AZA/biological therapy (P = 0.002), or smoking (P = 0.032) were independent predictors of disease behavior change. CONCLUSION: Our data suggest that perianal disease, small bowel disease, smoking, prior steroid use, early AZA or AZA/biological therapy are all predictors of disease behavior change in CD patients.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".