Benefit of rituximab maintenance after first-line bendamustine-rituximab in patients with mantle cell lymphoma
Bibliographic record
Abstract
ABSTRACT: The benefit of rituximab maintenance after first-line (1L) bendamustine-rituximab (BR) in patients with mantle cell lymphoma (MCL) remains uncertain, with inconsistent results from the phase 2 MAINTAIN trial and several retrospective studies. We conducted a large retrospective study at 27 US and Canadian academic centers to examine the benefit of rituximab maintenance after BR. A total of 911 patients received 1L BR between 2010 and 2020, and 703 had an objective response and no evidence of disease progression at the 3-month post-BR landmark. Among those, 394 (56%) received rituximab maintenance and 309 (44%) did not, with largely similar baseline patient and disease characteristics. In the landmark analysis, rituximab maintenance was associated with improved event-free survival (EFS; median, 49.9 vs 29.7 months; P< .001) as well as overall survival (OS; median, 109.5 vs 74.2 months; P< .001). The EFS and OS benefits were observed across most of the subgroups. EFS and OS differences were statistically significant in those who achieved a complete response to 1L BR (n = 590; median EFS, 62.7 vs 31.1 months [P< .001]; median OS, 136.1 vs 75.3 months [P< .001]), but the analysis among those who achieved a partial response to 1L BR was limited by the small sample size. These results provide additional evidence for the survival benefit of rituximab maintenance after BR in MCL and support its use in clinical trial design and routine practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".