Identifying Patients at High Risk of Loss of Response to Infliximab Maintenance Therapy in Paediatric Crohn’s Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Loss of response to infliximab resulting in discontinuation of therapy is a frequent problem encountered in paediatric Crohn's disease. Although identifying patients at risk of failure could have important implications for follow-up, literature in this area remains sparse. Our primary aim was to identify predictors of loss of response to infliximab among patients who were responders to induction. The secondary aim was to identify predictors of non-response to induction. METHODS: A retrospective cohort of patients with paediatric Crohn's disease treated with infliximab between 2000 and 2013 was followed until loss of response to infliximab or transfer to adult care. Predictors of response to induction therapy were studied by multivariate logistic regression. Time to treatment failure was analysed with a multivariate Cox model. RESULTS: Two-hundred and forty-eight patients were eligible for the study. Of these, 196 (79%) were responders to induction (57% clinical remission and 22% clinical response) and 52 (21%) were non-responders. Steroid resistance was the only variable independently associated with primary non-response (odds ratio [OR] 4.57, 95% confidence interval [CI] 1.67-12.50, p = 0.002). Thirty-one of the 196 responders discontinued infliximab due to loss of response after a mean of 1.6±1.3 years of treatment. Predictors of loss of response were level of response to induction (clinical response vs clinical remission, hazard ratio [HR] 3.74, 95% CI 1.80-7.80, p = 0.0004) and isolated colonic disease (HR 2.72, 95% CI 1.30-5.71, p = 0.008). CONCLUSIONS: Patients who fail to achieve clinical remission after induction and/or who have isolated colonic disease are at increased risk of loss of response to infliximab.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".