Real-world experience with subcutaneous infliximab: broadening treatment strategies for inflammatory bowel disease
Bibliographic record
Abstract
INTRODUCTION: The first subcutaneous (SC) formulation of infliximab (IFX), CT‑P13 SC, has been approved in Europe and Australia, including for the treatment of inflammatory bowel disease (IBD). AREAS COVERED: We provide a comprehensive overview of available clinical trial and real-world data for IFX SC treatment of IBD, focusing on the potential benefits of switching from IFX intravenous (IV) to IFX SC. We evaluate emerging evidence for IFX SC treatment for difficult-to-treat IBD, use as monotherapy, and suitability for patients receiving escalated IFX IV doses. Therapeutic drug monitoring approaches and patient and healthcare system perspectives on IFX SC are also discussed. EXPERT OPINION: IFX SC represents a significant treatment innovation in the tumor necrosis factor inhibitor class after approximately 20 years of IFX IV availability. Evidence suggests that IFX SC is well tolerated and is associated with high patient acceptance and satisfaction. In addition, effectiveness is maintained in patients with stable disease following switch from IFX IV. Switching may be advisable, given the clinical benefits of IFX SC and its potential to improve healthcare service capacity. There are several areas requiring further research, including the role of IFX SC in difficult-to-treat and refractory disease, and the feasibility of IFX SC monotherapy.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 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".