Maintenance of clinical benefit in Crohn’s disease patients after discontinuation of infliximab: long‐term follow‐up of a single centre cohort
Bibliographic record
Abstract
BACKGROUND: Tumour necrosis factor-blockade with infliximab has advanced the treatment of Crohn's disease. While infliximab is efficacious, it remains to be determined whether patients who enter clinical remission with an anti-tumour necrosis factor therapy can have their treatment stopped and retain the state of remission. AIM: To assess in patients with Crohn's disease who obtained infliximab-induced remission, the proportion who relapsed after infliximab discontinuation. METHODS: This longitudinal cohort study examined patients from a University-based IBD referral centre. Forty eight patients with Crohn's disease in full clinical remission and who then discontinued infliximab were followed up for up to 7 years. Crohn's disease relapse was defined as an intervention with Crohn's disease medication or surgery. RESULTS: Kaplan-Meier analysis of the proportion of patients with sustained clinical benefit demonstrated that 50% relapsed within 477 days after infliximab discontinuance. In contrast, 35% of patients remained well, and without clinical relapse, up to the end of the nearly 7-year follow-up. CONCLUSIONS: In patients with Crohn's disease with an infliximab-induced remission, stopping infliximab results in a predictable relapse in a majority of patients. Nevertheless, a small percentage of patients sustain a long-term remission.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".