Retreatment with anti-tumor necrosis factor therapy in combination with an immunomodulator for recurrence of Crohn’s disease after ileocecal resection results in prolonged continuation as compared to anti-tumor necrosis factor monotherapy
Bibliographic record
Abstract
BACKGROUND: A considerable proportion of Crohn's disease patients that undergo ileocecal resection (ICR) have failed anti-tumor necrosis factor (TNF) therapy preoperatively. This study aimed to assess the effectiveness of retreatment of anti-TNF therapy in patients with postoperative recurrence. METHODS: A real-world cohort study was performed on Crohn's disease patients who underwent primary ICR after anti-TNF therapy failure, and who were retreated with anti-TNF therapy for postoperative symptomatic Crohn's disease. The primary outcome was treatment failure (the need for (re)introduction of corticosteroids, immunosuppressants, or biologicals or the need for re-resection). Sub-analyses were performed on the nature of preoperative anti-TNF failure (primary non-response, secondary loss of response, intolerance), indication for ICR (refractory, stricturing, penetrating disease), combination therapy with immunomodulators, retreatment with the same anti-TNF agent and preoperative exposure to 1 vs. >1 anti-TNF agents. RESULTS: In total, 66 of 364 patients retreated with anti-TNF therapy following ICR. Cumulative rates of treatment failure at 1 and 2 years were 28% and 47%. Treatment failure rate at 2 years was significantly lower in patients receiving combination therapy as compared to anti-TNF monotherapy (30% vs. 49%, P = 0.02). No difference in treatment failure was found with regards to the nature of preoperative anti-TNF failure (P = 0.76), indication for ICR (P = 0.88) switch of anti-TNF agent (P = 0.55) agent, and preoperative exposure to 1 vs. >1 anti-TNF agents (P = 0.88). CONCLUSION: Retreatment with anti-TNF therapy for postoperative Crohn's disease recurrence is a valid strategy after preoperative failure. Combination therapy is associated with a lower rate of treatment failure.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".