Selective biologics for ulcerative colitis and Crohn's disease – clinical utility of vedolizumab
Bibliographic record
Abstract
Jill MV Petkau, Bertus Eksteen Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada Abstract: Inflammatory bowel disease (IBD) encompasses a cluster of different disease phenotypes which are broadly classified into ulcerative colitis and Crohn's disease. Disease pathogenesis is driven by abnormal host immune responses to their resident gut microbiome in genetically susceptible individuals. Clinical disease features and outcomes are heterogenous and not unexpected as over 163 genetic loci are associated with disease susceptibility, and there are great variability in environmental exposures. Despite this variability, there has been relatively few efficacious therapies for particularly moderate-to-severe IBD. Treatment has been dominated by antitumor necrosis alpha agents with significant success but equally potentially serious adverse events. Therapeutic targeting of leucocyte trafficking has emerged as a viable alternative therapy, with vedolizumab being the lead compound. This review focuses primarily on its biological function as a selective gut immunotherapy, its safety and efficacy, and its emerging role as a mainstream therapy in managing IBD. Keywords: adhesion molecule antagonist, anti-α4β7 integrin, inflammatory bowel disease, leukocyte trafficking, monoclonal antibody, selective gut immunotherapy, tumor necrosis factor alpha
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".