Bibliographic record
Abstract
BACKGROUND: The past decade has seen important advances in the management of chronic inflammatory bowel diseases (IBD), consisting of Crohn's disease (CD) and ulcerative colitis. The development of TNF antagonists, the recognition of interrupting lymphocyte trafficking as an effective treatment strategy, confirmation of the value of combination therapy, and the need, particularly in CD, for the treatment of high-risk patients early in the disease course are all fundamental concepts upon which the next generation of IBD treatment algorithms will be built. Emerging concepts that will continue to evolve and shape the field include an increased emphasis on personalized medicine (right drug, right dose, right time) and the development of new therapeutic classes. In this article, we review the clinical data and provide some insights into recent data regarding IBD therapies. KEY MESSAGES: In this article, we review the mechanism of action and data for novel therapies in IBD with particular focus on the evidence for agents targeting leukocyte trafficking, cytokine signaling, including interleukin-12/23 and the Janus kinase-signal transducers/activators of transcription pathway, and the emergence of antisense therapy for the treatment of IBD. CONCLUSIONS: Multiple new therapies are emerging for IBD; however, the potential positioning of these agents in treatment algorithms is difficult to predict in the absence of comparative effectiveness studies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".