Cost-effectiveness studies of brexu-cel for relapsed/refractory B-cell acute lymphoblastic leukemia and mantle cell lymphoma: a systematic review
Bibliographic record
Abstract
INTRODUCTION: This systematic review aims to explore the existing evidence on the cost-effectiveness of brexu-cel across different international jurisdictions. METHODS: A systematic search of articles on Embase, Medline, Econlit, Web of Science, Scopus, gray literature, and a manual search of HTA reports was done until 24 June 2024. Original English articles and reports from different countries assessing the cost-effectiveness of brexu-cel in relapsed/refractory acute lymphoblastic leukemia (R/R ALL) and mantle cell lymphoma (R/R MCL) were included. This review was registered in the Open Science Framework (OSF) registry. RESULTS: Of the 149 records, 22 articles underwent full-text review after the title and abstract screening, five met the inclusion criteria along with seven HTA reports from Australia, Canada, Scotland, and England. The CEA studies were from the US, England, Canada, and Italy, with varying perspectives, mainly adopting a partitioned survival model and lifetime horizons. The model input data from the ZUMA-2 and ZUMA-3 trials were used for brexu-cel, with comparisons from their respective trials or literature. CONCLUSION: Brexu-cel was found cost-effective in all the CEA studies and an HTA report from Scotland, but the other HTA agencies reported uncertainties around the cost-effectiveness of brexu-cel for R/R ALL and R/R MCL. REGISTRATION: Open Science Framework. (Reg doi: https://doi.org/10.17605/OSF.IO/JZU6Y).
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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 | 0.013 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".