Accelerating Earlier Access to Anti-TNF-α Agents with Biosimilar Medicines in the Management of Inflammatory Bowel Disease
Bibliographic record
Abstract
Data indicate that earlier initiation of anti-tumor necrosis factor alpha (anti-TNF-α) biologic medicines may prevent progression to irreversible bowel damage and improve outcomes for patients with inflammatory bowel disease (IBD), particularly Crohn's disease. However, the high cost of such therapies may restrict access and prevent timely treatment of IBD. Biosimilar anti-TNF-α medicines may represent a valuable opportunity for cost savings and optimized patient outcomes by improving access to advanced therapies and allowing earlier anti-TNF-α treatment initiation. Biosimilar anti-TNF-α medicines have been shown to offer consistent therapeutic outcomes to their reference medicines, yet despite entering the IBD treatment armamentarium over 10 years ago, their implementation in clinical practice remains suboptimal. Factors limiting the 'real' use of biosimilar anti-TNF-α medicines may include an ongoing lack of understanding and acceptance of biosimilars by both healthcare professionals (HCPs) and patients, as well as systemic factors such as formulary decisions outside of the control of the prescriber. In this review, an expert panel of gastroenterologists discusses HCP-level considerations to improve biosimilar anti-TNF-α utilization in IBD in order to support early anti-TNF-α initiation and maximize patient outcomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".