Current Evidence for Combined Targeted Therapy for the Treatment of Inflammatory Bowel Disease
Bibliographic record
Abstract
Biologicals and small molecules have revolutionized the medical management of inflammatory bowel diseases (IBD), yet they are only effective in a proportion of patients, and their impact on changing the natural history of the disease is still debatable. Recently, the concept of combining targeted biologics and small-molecule therapies has been introduced to the treatment of IBD. Dual-targeted therapy (sequential and combined), which is the combination of two targeted therapies, might be a reasonable choice for patients to break through the therapeutic ceiling. A recent randomized clinical trial (VEGA) provided the first controlled evidence that the short-term combination of two biological agents may lead to superior disease control than either of the agents alone in patients with ulcerative colitis (UC) without jeopardizing safety. Multiple studies are underway in both Crohn's disease and UC. Additionally, real-world evidence is accumulating in IBD patients receiving combination therapies with concomitant IBD and extraintestinal manifestations or in patients with medically refractory IBD. Of note, the majority of these patients were exposed to multiple biological agents earlier and lost response to at least one of the agents in the combination. This review summarizes current knowledge regarding this attractive novel therapeutic option in IBD. Clearly, more controlled data are needed to evaluate optimal timing, efficacy, and mitigation of safety concerns.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".