Does everyone with inflammatory bowel disease need to be treated with combination therapy?
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article describes the evolution of combination therapy in inflammatory bowel disease and how to determine when combination may not be necessary. RECENT FINDINGS: The combination of an immunosuppressive agent with an antibody to tumor necrosis factor has proven to be more effective than monotherapy with either an immunosuppressive agent or an antibody to tumor necrosis factor alone. The emergence of therapeutic drug monitoring has identified the importance of maintaining adequate circulating drug levels and suppressing antibody formation to the biological agent. The concomitant use of an immunosuppressive agent can help effect this. Although this may be optimal therapy in moderately ill patients. I review why there still remains a role for using monotherapy with immunosuppressive agents, as well as consideration of therapy withdrawal. SUMMARY: Although combination therapy is the treatment of choice in persons with moderate to severe disease or who have lost response to biological monotherapy, there remains a role for monotherapy with immunosuppressive agents. Though the newer biological therapies are mostly not used in combination, this may be a future approach considering their response rates are also correlated with higher drug levels. Many persons with inflammatory bowel disease can remain well on an immunosuppressive agent alone and some can even maintain a longstanding remission off all therapy.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".