Trough serum infliximab: a predictive factor of clinical outcome for infliximab treatment in acute ulcerative colitis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Antibodies to infliximab reduce serum infliximab with loss of clinical benefit, but undetectable trough serum concentrations of infliximab may occur without antibody formation. The relationship between trough serum infliximab and clinical outcomes was evaluated in acute ulcerative colitis. METHODS: In a cohort of 115 patients with ulcerative colitis treated with three-dose induction followed by scheduled maintenance infliximab, rates of clinical remission, colectomy, antibodies to infliximab and trough serum infliximab were determined. RESULTS: Rates of remission were 32% at week 10 and 37% at week 54. Colectomy occurred in 40% of patients, at a median of 5.3 (IQR 1.9-12.1) months. Detectable trough serum infliximab was present in 39% of patients and, among patients with undetectable infliximab, 41% were antibody positive and 20% were antibody negative. For antibody-positive and antibody-negative patients, rates of remission (18% vs 14%), endoscopic improvement (25% vs 35%) and colectomy (52% vs 59%) were not different. A detectable serum infliximab was associated with higher rates of remission (69% vs 15%; p<0.001) and endoscopic improvement (76% vs 28%, p<0.001). An undetectable serum infliximab predicted an increased risk for colectomy (55% vs 7%, OR 9.3; 95% CI 2.9 to 29.9; p<0.001). Concurrent immunosuppression was not associated with clinical outcomes. CONCLUSIONS: For patients with ulcerative colitis treated with infliximab, a detectable trough serum infliximab predicts clinical remission, endoscopic improvement and a lower risk for colectomy. An undetectable trough serum infliximab, irrespective of antibody status, is associated with less favourable outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".