An international consensus on the design of clinical trials for advanced combination treatment (ACT) in inflammatory bowel disease
Bibliographic record
Abstract
Background: Advanced Combination Treatment (ACT) refers to the dual use of two advanced therapies-either two biologics, two small molecules, or one biologic and one small molecule. There is a lack of guidance regarding clinical trial design for ACT in patients with inflammatory bowel disease (IBD). Key uncertainties remain regarding aspects such as eligibility criteria, pharmacotherapy regimens, safety considerations, and standardised trial design configurations for both induction and maintenance phases. We aimed to formulate expert recommendations regarding the design of ACT clinical trials in IBD. Methods: A systematic search was performed in June 2023. Modified RAND/University of California, Los Angeles Appropriateness Methodology (RAM) was employed to evaluate 287 statements related to the design of ACT clinical trials in patients with IBD. A multidisciplinary panel of gastroenterologists and precision medicine scientists rated statement appropriateness on a 9-point Likert scale. Statements were subsequently categorised as appropriate, uncertain, or inappropriate based on the median panel rating and the presence of disagreement. The consensus meetings were held on February 6, 2024 and June 4, 2024. Findings: ACT should consist of drugs with distinct mechanisms of action, avoiding combinations targeting the same biological pathway. Appropriate eligibility criteria included prior treatment failure and high risk for disease complications. Safety considerations were prioritised, with short-term use of high-risk regimens acceptable for induction therapy. Trial designs should compare ACT to monotherapy and allow for longitudinal evaluation. Co-primary endpoints of clinical remission and endoscopic response were endorsed, with safety outcomes including adverse events and infections. Precision medicine approaches, guided by biomarker analysis, were considered essential for further defining mechanistic pathways and monitoring treatment response. Interpretation: Implementing standardised design elements for eligibility criteria, pharmacotherapy regimens, safety considerations, and trial design configurations will facilitate the conduct of efficient clinical trials of ACT. Funding: None.
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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 | 0.432 | 0.394 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.019 | 0.016 |
| Research integrity | 0.031 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".