Axicabtagene ciloleucel compared to standard of care in Canadian patients with relapsed or refractory large B-cell lymphoma: a cost-effectiveness analysis of the ZUMA-7 trial
Bibliographic record
Abstract
AIMS AND BACKGROUND: In the pivotal ZUMA-7 trial, second-line (2L) treatment with axicabtagene ciloleucel (axi-cel) had superior clinical outcomes compared to standard of care (SOC; salvage chemoimmunotherapy followed by high-dose therapy and autologous stem cell transplant in responders) in patients with large B-cell lymphoma (LBCL) who were refractory or relapsed (r/r) within 12 months of completion of frontline therapy. The aim of this analysis was to evaluate the cost-effectiveness of axi-cel compared to SOC for 2L LBCL in Canada. METHODS: A 3-health state partitioned-survival model was used to estimate the cost-effectiveness of axi-cel vs. SOC from a Canadian healthcare system perspective. Clinical outcomes were informed by ZUMA-7. The model calculated expected quality-adjusted life years (QALYs), total costs, and the incremental cost-effectiveness ratio (ICER). RESULTS: Over a lifetime horizon, the model estimated a total of 9.48 and 7.25 QALYs, and total costs of $569,168 and $337,906 for axi-cel and SOC, respectively, resulting in an ICER of $103,810/QALY. When adjusting for the substantial proportion of patients in the SOC arm who received cellular therapy as subsequent treatment, the ICER was reduced to $78,555/QALY. CONCLUSIONS: Treatment with axi-cel in 2L is a cost-effective option that addresses an important unmet clinical need for Canadian patients with r/r LBCL.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".