A practical guide to combination advanced therapy in inflammatory bowel disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: To provide an overview of the current literature regarding the use of advanced combination therapy (ACT) in patients with inflammatory bowel disease (IBD). Although the treatment of IBD has come a long way, many patients do not respond or will lose response to currently available treatments over time. ACT has been proposed as a model to create sustained remission in difficult-to-treat IBD patient populations. This review discusses the available literature supporting the use of ACT, followed by practical tips for applying this model of treatment to clinical practice. RECENT FINDINGS: Both observational and controlled evidence have demonstrated that there may be an increased benefit of ACT in specific IBD patient populations compared to advanced targeted immunomodulator (TIM) monotherapy. Additional data is required to understand how to best use combination TIMs and the long-term risks associated with this strategy. SUMMARY: While the literature has demonstrated the potential for benefit in both Crohn's disease and ulcerative colitis, the use of ACT is currently off-label and long-term controlled data is needed. The successful application of ACT requires careful consideration of both patient and disease profiles as well as close monitoring of treatment response and adverse events.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.024 |
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".