Real-World Eligibility for Second-Line Chimeric Antigen Receptor T Cell Therapy in Large B Cell Lymphoma: A Population-Based Analysis
Bibliographic record
Abstract
The ZUMA-7 trial demonstrated the superiority of second-line chimeric antigen receptor (CAR) T cell therapy over standard of care chemotherapy with or without autologous stem cell transplantation (ASCT) for relapsed/refractory (r/r) large B cell lymphoma (LBCL). We conducted a retrospective population-based analysis to determine eligibility for second-line CAR-T cell therapy in the real-world setting. Among 125 patients with r/r LBCL between 2015 and 2019, 82% progressed within 12 months of first-line chemoimmunotherapy (CIT), 40% were treated with intention-to-transplantation, 22% underwent ASCT, and 7% achieved a durable remission after ASCT. With a median follow-up of 2.8 years, the median overall survival (OS) was 5.1 months, and 3-year OS was 15% (95% confidence interval [CI], 7% to 20%) for all patients and 10% (95% CI, 5% to 17%) for those progressing within 12 months of CIT. Although only 14% of patients met all the ZUMA-7 study inclusion criteria, as many as 65% of patients progressing within 12 months of CIT had adequate performance status to be considered potentially eligible for second-line CAR T cell therapy. Whereas the current standard of care results in poor outcomes for most patients with r/r LBCL, the use of CAR T cell therapy in second-line therapy could substantially increase the proportion of patients able to receive curative-intent treatment at first progression of 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".