Long Term Responses to Fludarabine and Rituximab in Waldenstrom’s Macroglobulinemia
Bibliographic record
Abstract
Abstract Fludarabine and rituximab are commonly used in combination in the treatment of Waldenstrom’s macroglobulinemia (WM), though long term outcome of this regimen remains to be defined. We therefore examined the outcome of 43 WM patients treated on a clinical trial whose eligibility included < 2 prior therapies, and no previous nucleoside analogue or rituximab treatment. Therapy consisted of 6 cycles (25 mg/m2/day for 5 days) of fludarabine and 8 infusions (375 mg/m2/week) of rituximab over 31 weeks. 43 patients were enrolled with a median age of 61, and median prior therapies of 0. Responses were: CR (n=2); VGPR (n=14); PR (n=21); MR (n=4); for an overall and major response rate of 95.3% and 86.0%, respectively. At best response, median bone marrow disease involvement declined from 55% to 5% (p<0.00001), while serum IgM decreased from 3,840 to 443 mg/dL (p<0.00001), and hematocrit rose from 31.2% to 38.0% (p<0.0008). The median time to progression for all patients was 51.2 months, and was longer for untreated versus previously treated patients (77.6 vs. 38.4 months; p=0.017), as well as for those patients who achieved ≥ VGPR versus 88.3 vs. 36.9 months; p=0.049). Grade ≥ 3 toxicities included neutropenia (n=27); thrombocytopenia (n=7); pneumonia (n=6), including two patients who succumbed to non-PCP interstitial pneumonia; peripheral neuropathy (n=2); limbic encephalitis (n=1); hemolytic anemia (n=1). With a median follow-up of 40.3 months, we observed transformation to aggressive lymphoma (n=3); myelodysplasia (n=1); acute myelogenous leukemia (n=2); bladder carcinoma (n=1); and carcinoma of unknown primary (n=1) among 8 patients. The results of this study demonstrate that fludarabine and rituximab is an active regimen in WM, though short and long term toxicities need to be carefully weighed against other available treatment options.
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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) | 0.001 | 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 | 0.001 |
| 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".