Budget impact analysis of breast cancer medications: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Breast cancer (BC) is the most common cancer globally among women, with 2,261,419 new cases in 2020; systemic treatment may be neo-adjuvant, adjuvant, or both. BC subtype guides the standard systemic therapy administered, which consists of endocrine therapy for all HR + tumors, trastuzumab-based HER2-directed antibody therapy plus chemotherapy for all HER2 + tumors (with endocrine therapy given in addition, if concurrent HR positivity), and chemotherapy alone for the triple-negative subtype. This study aimed to identify, evaluate, and systematically review all budget impact analyses (BIAs) of BC medications worldwide. METHODS: PubMed, Scopus, and Web of Science Core Collection databases were thoroughly searched up to 26th March 2022 to identify original published studies which evaluate BIA of BC medications. ISPOR Task Force guidelines were used to assess the quality of included studies. This study was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: In total, 17 BIAs were included in the study. About half of the studies were conducted in Europe. The results of the BIAs showed that most of the included BIAs are conducted from the payer's perspective; they have different methodological frameworks for recommended chemotherapy, targeted therapy, and immunotherapy agents to treat BC. For the same medications, the results of budgetary effects are not consistent in diverse countries. Nine out of the 17 studies were focused on trastuzumab, in which the biosimilar form reduced costs, but the brand form increased costs, especially in a 52-week treatment period. CONCLUSION: Researchers should conduct the budget impact analysis of high-value medications such as anti-tumor drugs more objectively, and the accuracy of parameters needs to be more strictly guaranteed. Furthermore, it is worthy of declaring that the budgetary impact of the same drug is not always consistent over time, so the researchers should measure access to medication in the long run.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it