Beyond Cost: Observations on Clinical and Patient Benefits of Biosimilars in Real-World Settings
Bibliographic record
Abstract
The introduction of biosimilars into healthcare systems globally is recognized by many as a healthcare success. Despite this, questions have been raised about whether biosimilars can deliver sufficient value to patients and healthcare professionals, as well as sufficient cost saving, for their use in treatment to be worthwhile. In this review, we discuss how the increasing financial burden of complex therapeutic medicines, such as biologics, can be ameliorated by off-patent biosimilar medicines, particularly with increasing worldwide incidences of cancer and other chronic diseases. We then describe real-world cases that demonstrate the significant direct and indirect benefits of biosimilars to patients and healthcare systems beyond costs. Healthcare sustainability is crucial to ensuring that healthcare systems can continue to deliver high-quality care to patients. The savings realized from the introduction of biosimilars have expanded treatment options and improved access to therapies across a spectrum of diseases. Cost savings from biosimilar use have also led to changes in treatment guidelines, increasing the availability of biologic medicines for earlier lines of therapy. This expansion of access can have a positive impact on the overall patient experience and can reduce the overall disease burden. However, the adoption of biosimilars has not been universally successful, and faces challenges in the current healthcare landscape and in the pharmaceutical development pipeline.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".