The future of inflammatory bowel disease management: Combining progress in trial design with advances in targeted therapy
Bibliographic record
Abstract
Anti-tumour necrosis factor antagonists have appreciably improved patient outcomes in Crohn's disease, shifting the goals of treatment from control of symptoms to clinical remission (Crohn's disease activity index <150) combined with mucosal healing - the new concept of 'deep remission'. Achieving deep remission brings clinically meaningful benefits, including reduced hospitalization and reduced need for surgery. Aspects such as the dose, timing and intensification of anti-tumour necrosis factor therapy affect the likelihood of achieving deep remission, but definitive evidence on long-term benefits and the risk/benefit profile of treatment intensification is needed. A consequence of the success of anti-tumour necrosis factor therapies has been a change in the disease characteristics of the patient population entering clinical trials. Therefore, new clinical study paradigms, such as cluster randomization and therapeutic strategy trials, are needed. High placebo response rates and the ethics of testing emerging agents against placebo in an era of effective therapies are challenges to traditional randomized controlled trials. Overcoming these challenges will not only help to optimize anti-tumour necrosis factor therapy, but also advance development of emerging treatments for Crohn's disease.
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".