Addition of rituximab to fludarabine may prolong progression-free survival and overall survival in patients with previously untreated chronic lymphocytic leukemia: an updated retrospective comparative analysis of CALGB 9712 and CALGB 9011
Bibliographic record
Abstract
Fludarabine and rituximab combination therapies in chronic lymphocytic leukemia (CLL) have yielded promising early results, but no comparative efficacy data relative to standard fludarabine treatment regimens have been reported. To assess the effect of the addition of rituximab to fludarabine therapy, we retrospectively compared the treatment outcome of patients with similar clinical characteristics enrolled on 2 multicenter clinical trials performed by the Cancer and Leukemia Group B and the US Intergroup that used fludarabine and rituximab (CALGB 9712, n = 104) or fludarabine (CALGB 9011, n = 178). In multivariate analyses controlling for pretreatment characteristics, the patients receiving fludarabine and rituximab had a significantly better progression-free survival (PFS; P < .0001) and overall survival (OS; P = .0006) than patients receiving fludarabine therapy. Two-year PFS probabilities were 0.67 versus 0.45, and 2-year OS probabilities were 0.93 versus 0.81. Infectious toxicity was similar between the 2 treatment approaches. These comparative data are retrospective and could be confounded by differences in supportive care or dissimilar enrollment of genetic subsets on each trial. Confirmation of these findings will require a prospective randomized trial comparing fludarabine and rituximab to fludarabine.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".