Review article: dose optimisation of infliximab for acute severe ulcerative colitis
Bibliographic record
Abstract
BACKGROUND: Although optimal medical management of acute severe ulcerative colitis (UC) is ill-defined, infliximab has become a standard of care. Accumulating evidence suggests an increased rate of infliximab clearance in patients with acute severe UC and a reduced colectomy rate with an intensified infliximab induction regimen. AIM: To assess the strength of the current evidence for the relationship between infliximab pharmacokinetics, dosing strategies and disease behaviour in patients with acute severe UC. METHODS: We systematically searched MEDLINE and conference proceedings from 2000 to 2016 for relevant articles describing the pharmacokinetics of infliximab in acute severe UC and/or infliximab dose intensification strategies in acute severe UC. Eligible articles described randomised controlled trials, and cohort, cross-sectional, and case-controlled studies. RESULTS: Of 400 citations identified, 76 studies were eligible. Increased infliximab clearance occurs in patients with acute severe UC, and is driven by the total inflammatory burden and leakage of drug into the colonic lumen. Several cohort studies suggest that infliximab dose intensification is beneficial to at least 50% of acute severe UC patients and the results of case-controlled studies indicate that an intensified infliximab dosing regimen with 1-2 additional infusions in the first 3 weeks of treatment could reduce the early (3-month) colectomy rate by up to 80%, although these data require prospective validation. CONCLUSIONS: Uncontrolled studies suggest a benefit for infliximab dose optimisation in patients with acute severe UC. A randomised controlled trial in acute severe UC patients comparing a personalised infliximab dose-optimisation strategy with conventional dosing is a research priority.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".