Bendamustine and rituximab is well‐tolerated and efficient in the treatment of indolent non‐Hodgkin<scp>'</scp>s lymphoma and mantle cell lymphoma in elderly: A single center observational study
Bibliographic record
Abstract
Bendamustine and rituximab (BR) is a preferred first-line therapy for indolent non-Hodgkin's lymphoma (iNHL) and mantle cell lymphoma (MCL); however, few reports on BR performance in elderly patients are available to date. We compared safety and efficacy of BR in patients ≥70 years (elderly) vs <70 years (younger) treated at our institution. Among 201 patients, 113 were elderly (median age: 77 years), including 38 patients ≥80 years, and 88 were younger (median age: 62 years). Elderly patients had more bone marrow involvement by lymphoma, anemia, ECOG status 3 and high-risk disease follicular lymphoma (P < .05 for all). Fifty-four percent of elderly received full dose of bendamustine vs 79.5% of younger patients. More elderly patients (54%) vs younger (43.2%) experienced treatment delay. Less elderly proceeded to rituximab maintenance. Overall, the number of adverse events per patient and transformed B-Cell lymphoma/secondary malignancies were similar between groups. Elderly patients had less febrile neutropenia, rituximab-associated infusion reactions, but more herpes zoster reactivation. There were more deaths in the elderly (37.2%) vs younger (10.2%) groups (P < .001), mainly due to non-lymphoma-related causes. With median follow-up of 42 months [4.0-97.0] disease-free survival for the elderly was similar to younger patients. There was no difference between patients <80 and ≥80 years (P = .274). In conclusion, the real-world elderly patients have more advanced disease and higher ECOG status. BR is well-tolerated; elderly patients had lower incidence of febrile neutropenia. Dose reduction and treatment delays are common, but BR efficacy was not affected even in very old patients (≥80 years).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".