Novel concepts in inflammatory bowel disease
Bibliographic record
Abstract
INTRODUCTION: Clinical management in inflammatory bowel disease (IBD) is constantly changing. Although improvement in symptoms is of paramount importance, using this as the only surrogate marker of disease activity might underestimate disease burden. SOURCES OF DATA: New data from randomized clinical trials are now available. Treatment paradigms are constantly changing leading to an evolution in the therapeutic approach in routine IBD practice. AREAS OF AGREEMENT: Patients with an aggressive disease phenotype should be identified at the onset and treated more intensely in order to achieve long-lasting mucosal remission. AREAS OF CONTROVERSY: Patients who have mild and indolent disease need to be identified and not over treated. GROWING POINTS: The primary endpoint in IBD management should ideally be mucosal healing. Ample data are now available that correlates mucosal healing with surgical-free outcomes with minimal intestinal damage and patient disability. However, the exact degree of mucosal healing that will lead to improved long-term remission, decreased hospital and surgical rates remains unknown. AREAS TIMELY FOR DEVELOPING RESEARCH: Clinical translational work is needed to identify novel pathways in IBD pathogenesis that sub-select patients who would benefit by specific-cytokine pathway modulation.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".