Matching-adjusted indirect comparison of lisocabtagene maraleucel versus axicabtagene ciloleucel for second-line treatment of patients with early relapsed or refractory large B-cell lymphoma
Bibliographic record
Abstract
In the absence of head-to-head trials of chimeric antigen receptor T-cell therapies, a matching-adjusted indirect comparison (MAIC) evaluated the comparative efficacy and safety of lisocabtagene maraleucel (liso-cel) versus axicabtagene ciloleucel (axi-cel) as second-line (2L) therapies in patients with early relapsed or refractory (R/R) large B-cell lymphoma (LBCL). Data sources were the pivotal phase 3 trials: individual patient-level data from TRANSFORM [liso-cel (NCT03575351), N = 184] and summary-level data from ZUMA-7 [axi-cel (NCT03391466), N = 359]. Efficacy analyses were anchored via the common standard-of-care comparator arms and showed no statistically significant differences. Safety analyses were unanchored, showing liso-cel had lower odds (95% confidence interval) of grade ≥3 serious treatment-emergent adverse events [0.49 (0.27–0.90)], cytokine release syndrome [any grade, 0.09 (0.04–0.18); grade ≥3, 0.09 (0.01–0.75)], and neurological events [any grade, 0.08 (0.03–0.18); grade ≥3, 0.21 (0.06–0.68)]. In this MAIC, liso-cel demonstrated comparable efficacy and a more favorable safety profile versus axi-cel for 2L treatment of 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".