Systematic Review and Meta-analysis: Loss of Response and Need for Dose Escalation of Infliximab and Adalimumab in Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND: Loss of response to infliximab or adalimumab in ulcerative colitis occurs frequently, and dose escalation may aid in regaining clinical benefit. This study aimed to systematically assess the annual loss of response and dose escalation rates for infliximab and adalimumab in ulcerative colitis. METHODS: A systematic search was conducted from August 1999 to July 2021 for studies reporting loss of response and dose escalation during infliximab and/or adalimumab use in ulcerative colitis patients with primary response. Annual loss of response, dose escalation rates, and clinical benefit after dose escalation were calculated. Subgroup analyses were performed for studies with 1-year follow-up or less. RESULTS: We included 50 unique studies assessing loss of response (infliximab, n = 24; adalimumab, n = 21) or dose escalation (infliximab, n = 21; adalimumab, n = 16). The pooled annual loss of response for infliximab was 10.1% (95% confidence interval [CI], 7.1-14.3) and 13.6% (95% CI, 9.3-19.9) for studies with 1-year follow-up. The pooled annual loss of response for adalimumab was 13.4% (95% CI, 8.2-21.8) and 23.3% (95% CI, 15.4-35.1) for studies with 1-year follow-up. Annual pooled dose escalation rates were 13.8% (95% CI, 8.7-21.7) for infliximab and 21.3% (95% CI, 14.4-31.3) for adalimumab, regaining clinical benefit in 72.4% and 52.3%, respectively. CONCLUSIONS: Annual loss of response was 10% for infliximab and 13% for adalimumab, with higher rates during the first year. Annual dose escalation rates were 14% (infliximab) and 21% (adalimumab), with clinical benefit in 72% and 52%, respectively. Uniform definitions are needed to facilitate more robust evaluations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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) | 0.000 | 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".