Efficacy and Safety of Bendamustine‐Rituximab for Nodular Lymphocyte Predominant Hodgkin Lymphoma
Bibliographic record
Abstract
INTRODUCTION: Bendamustine-rituximab (BR) has demonstrated efficacy and tolerability in the treatment of various indolent B-cell lymphomas, but there is limited data regarding its outcomes in nodular lymphocyte predominant B-cell (Hodgkin) lymphoma (NLPBL). METHODS: This retrospective, population-based study included consecutive patients ≥ 18 years old who received BR for NLPBL in Alberta, Canada. RESULTS: The study population comprised 23 patients with a median age of 39 years (range 18-74) who received BR for treatment-naive (n = 17) or relapsed/refractory (n = 6) NLPBL. Advanced stage disease was present in 18 (78%), splenic involvement in 9 (39%), and bone marrow involvement in 8 (35%) patients. The response rate to BR was 100%, with a complete response in 78% and a partial response in 22%. After a median follow-up of 4.3 years (range 0.8-9.0), the 4-year progression-free survival and overall survival rates were 83% (95% CI: 55%-94%) and 87% (95% CI: 58%-97%) for all patients and 84% (95% CI: 50%-96%) and 91% (95% CI: 51%-99%) for recipients of first-line BR, respectively. The safety profile was in keeping with the known toxicities of BR, with the most common adverse events consisting of neutropenia, rash, and infusion reactions. CONCLUSION: These findings suggest that BR is a promising treatment option for NLPBL, with a favorable efficacy and safety profile.
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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 | 0.000 |
| 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".