Careful patient selection may improve response rates to infliximab in inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND AND AIM: The use of infliximab in the treatment of Crohn's disease (CD) is acceptable and appears to be effective in ulcerative colitis (UC). Careful patient selection, resulting in infliximab only for truly refractory inflammatory bowel disease (IBD), may improve its efficacy. The present study aimed to determine if careful patient selection improved infliximab efficacy in IBD. METHODS: CD or UC/IBD unclassified patients (Montreal classification) were considered for infliximab treatment only after failure of disease control with conventional therapies and confirmation of active disease. Patients with purely luminal IBD received a single infliximab dose. Patients with fistulizing disease (with or without luminal disease) received infliximab at 0, 2 and 6 weeks. Changes to Harvey Bradshaw (HBI) for inflammatory CD and Colitis Activity Index (CAI) for UC/IBDU were used to determine the response and remission rates. In fistulizing CD, a remission was sustained cessation of drainage and resolution of the fistula. Response was correlated to inflammatory marker levels. RESULTS: Seventy IBD patients were treated. In CD, 85.2% (46/54) had active luminal and 40.7% (22/54) had fistulizing disease. In luminal CD, at 8 weeks a single infliximab dose induced remission in 75% (24/32) of patients compared to 92.9% (13/14) after infliximab at 0, 2 and 6 weeks. Fistulizing disease responded in 77.2% (17/22) and remitted in 50% (11/22) of patients at 8 weeks. In UC/IBDU, 75% (12/16) responded and 43.8% (7/16) of patients were in remission at 8 weeks. CONCLUSION: Careful patient selection may improve infliximab's efficacy and clinical remission appears greater after induction with three infliximab doses in CD. Clinical efficacy is suggested for UC/IBDU.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".