Bibliographic record
Abstract
In assessing the best evidence for optimizing management of inflammatory bowel disease (IBD), the focus is typically on anti-inflammatory agents and therapies that modulate the immune system. The intestinal immune response remains the key focus of developing therapies as well. In the past decade, the concept of dysbiosis of the gut microbiome has emerged as a potential pathogenetic focus in IBD, and with this a burgeoning interest in manipulating the microbiome as a means of controlling the disease has emerged. In this review, anti-inflammatory, immune-modulating, and microbiome-modulating therapies will be covered in terms of what is known today, as well as treatments that may be part of the therapeutic armamentarium in the near future. Concurrent with the evolution of our understanding of the basic biology of IBD, there is an increasing appreciation for the disconnect between patients' symptoms and inflammatory disease. As clinical trials have simultaneously addressed both symptom scores and mucosal healing, investigators and clinicians have gained a greater appreciation for the fact that many symptoms may not be driven by active inflammation, and hence focusing only on immunomodulatory therapies would not serve patients' needs fully. Furthermore, there is an emerging recognition of the importance of stress and psychological health in symptom experience and treatment needs. In this review, approaches to managing patients' symptoms as well as other adjunctive approaches to improving well-being will also be discussed. Finally, throughout this review, important research questions regarding different aspects of treatment will be proposed.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".