Benefit of axicabtagene ciloleucel versus chemoimmunotherapy in older patients and/or patients with poor <scp>ECOG</scp> performance status with relapsed or refractory large B‐cell lymphoma after 2 or more lines of prior therapy
Bibliographic record
Abstract
Axicabtagene ciloleucel (axi-cel) in trials has demonstrated favorable efficacy compared with historical controls after ≥2 lines of therapy for the treatment of relapsed or refractory (R/R) large B cell lymphoma (LBCL). Herein, we compared the real-world effectiveness of axi-cel with efficacy and effectiveness of chemoimmunotherapy (CIT) in patients aged ≥65 years and patients with Eastern Cooperative Oncology Group performance status (ECOG PS) of 2. A total of 1146 patients treated with commercial axi-cel for R/R LBCL with ≥2 lines of prior therapy were included from the Center for International Blood and Marrow Transplantation Research prospective observational study, and 469 patients treated with CIT for R/R LBCL after ≥2 lines of prior therapy were included from SCHOLAR-1 (an international, multicohort, retrospective study). After propensity score matching, at a median follow-up of 24 months for patients receiving axi-cel and 60 months for patients receiving CIT, 12-month overall survival rates were 62% and 28%, respectively (hazard ratio, 0.30 [95% CI, 0.24-0.37]). Objective response rate (ORR) was 76% (complete response [CR] rate 58%) in patients receiving axi-cel versus 28% (CR rate 16%) for those receiving CIT. A 57% difference in ORR (55% difference in CR rate) favoring axi-cel over CIT was observed among patients aged ≥65 years. Increased magnitude of benefit in response rates for axi-cel versus CIT was also observed among patients with ECOG PS = 2. These findings further support the broader use of axi-cel in older patients and patients with ECOG PS = 2 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".