Biological Therapies in Immune-Mediated Inflammatory Diseases: Can Biosimilars Reduce Access Inequities?
Bibliographic record
Abstract
Biological therapies are an effective treatment for a range of immune-mediated inflammatory diseases (IMIDs), including rheumatoid arthritis, psoriasis, and inflammatory bowel diseases. However, due to their high costs, considerable differences in their utilization exist across the world, even among the various European countries, with many countries restricting access despite professional society guideline recommendations. Adoption of biologics by healthcare providers has been particularly poor in many Central and Eastern European countries. Differences in utilization have also been observed across medical specialties, healthcare providers, and at a regional and national level. The objective of this paper is to provide an overview of the different market access policies for biologics in Europe and to investigate reasons for such differences. One of the potential solutions for providing broader access to IMID patients, where cost is the major barrier, is to encourage the use of biosimilars in place of their reference products. Biosimilars are generally less expensive alternatives to already licensed biological therapies and are approved on the basis that they are similar to the reference product in terms of quality, safety, and efficacy. Budget impact models predict considerable cost savings following the introduction of biosimilars in the next few years. These savings could be used to increase access to biologics and other innovative therapies.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".