Higher Rates of Dose Optimisation for Infliximab Responders in Ulcerative Colitis than in Crohn’s disease
Bibliographic record
Abstract
BACKGROUND: Studies have demonstrated the benefit of dose optimisation in the setting of secondary loss of response to infliximab in inflammatory bowel disease. AIM: The aim of our study was to retrospectively investigate the rates of dose optimisation in an inflammatory bowel disease cohort receiving maintenance infliximab therapy to determine if there are different rates of dose optimisation between CD and UC cases and what impact this has on the durability of treatment effect. METHODS: Cases receiving infliximab for treatment of IBD between January 2008 and February 2014 were identified from an infusion centre database. Cases receiving ≥ 4 infusions were included in the study. Details of infusion dosing and timing were obtained. A dose increase from 5mg/kg to 10mg/kg or a reduction in the dosing interval was considered a dose optimisation. RESULTS: A total of 412 cases were included in the study; 52.7% required at least one dose optimisation. Dose optimisation was more common in UC than in CD cases [67.2% vs 46.3%, p = 0.00006]. The median time to dose optimisation was 7 months (95% confidence interval [CI] 4.8-9.2) for UC cases and 27 months [95% CI 7.3-46.7] for CD cases, p = 0.00003. CONCLUSIONS: Here we have shown that dose optimisation is required more frequently in UC than in CD, with a significantly shorter time to dose optimisation for UC cases than CD cases. The majority of cases responding to induction therapy with infliximab will have a sustained response to therapy, but over 50% will require a dose optimisation during their treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".