Advanced combination therapy: is it the best way to break the therapeutic ceiling?
Bibliographic record
Abstract
Current therapeutic strategies for inflammatory bowel disease (IBD) have reached a plateau in the rates of response and/or remission achieved with a single therapeutic agent. Consequently, the advanced combination therapy (ACT) strategy has emerged as a novel treatment concept for IBD. ACT involves the use of two different targeted therapies, whether biologic or small molecules, with the primary goal of overcoming the therapeutic plateau. Real-world evidence is accumulating among patients undergoing ACT, especially those dealing with concurrent IBD and extraintestinal manifestations or grappling with medically refractory IBD. The recently conducted VEGA study, a randomized clinical trial, has provided crucial insights by demonstrating that the short-term combination of dual biological agents can lead to superior disease control compared to single agents in patients diagnosed with ulcerative colitis (UC). This suggests that ACT holds promise as a therapeutic option to enhance disease control effectively. However, there is still limited evidence of ACT in UC patients who have proven refractory to biologic therapy and patients with Crohn's disease. This review aims to discuss whether ACT represents the optimal approach for overcoming the therapeutic ceiling in IBD.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".