Azathioprine or Ileocolic Resection for Steroid-Dependent Terminal Ileal Crohn's Disease? A Markov Analysis
Bibliographic record
Abstract
INTRODUCTION: The objective of this study was to determine whether initial azathioprine therapy, followed by ileocolic resection if azathioprine fails, or initial ileocolic resection without a trial of azathioprine is the preferred treatment strategy in steroid-dependent, terminal ileal Crohn's disease. METHODS: A Markov, decision analytic model was developed to simulate a 36-month course for a patient with steroid-dependent, terminal ileal Crohn's disease who would initially take azathioprine or have ileocolic resection. Clinically important outcomes in the model included side effects and effectiveness of azathioprine and postoperative complications, mortality, and recurrence following ileocolic resection. The probabilities and utilities for these variables were derived from previously published studies. RESULTS: Initial azathioprine therapy offered a relatively small benefit of 0.45 quality-adjusted life-months over initial ileocolic resection. The model was sensitive to utility for being symptom-free on azathioprine and utility for being symptom-free postoperatively. CONCLUSIONS: Initial azathioprine therapy and initial ileocolic resection are both reasonable treatment strategies in this setting. The preferred treatment strategy is highly dependent on the quality of life that can be achieved with each treatment option. Therefore, individual response and symptom control with each treatment must be strongly considered in this treatment decision.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".