Pharmacoeconomics of Cancer Therapies: Considerations With the Introduction of Biosimilars
Bibliographic record
Abstract
Biologics are important treatments for a number of cancers, but they are also significant drivers of globally escalating healthcare costs. Biosimilars have the potential to offer cost-savings with comparable efficacy and safety to innovator products. They are being used in the European Union, Canada, Japan, and Australia and may help with improving health outcomes while minimizing costs to patients and global healthcare systems. The overall value of a biosimilar is not determined solely by its pricing. Efficacy and safety relative to the reference biologic drug and competitive agents as well as development and manufacturing costs, treatment administration costs, and results from long-term safety monitoring are considered. Optimizing economic efficiency is one part of an ongoing healthcare decision-making process with all therapeutics that aims to attain high levels of quality-of-care and safety given available resources. Some analytic tools stakeholders use to determine the pharmacoeconomic value of a therapy that are highlighted in this review article are opportunity cost, cost-effectiveness, and cost-minimization analyses. These methodologies can provide information to physicians, patients, and payers that may help reaffirm the value of a given biosimilar compared with its reference product throughout its life cycle.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| 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".