Rituximab-based combination therapy in patients with Waldenström macroglobulinemia: a systematic review and meta-analysis
Bibliographic record
Abstract
Yan-Hua Zheng, Li Xu, Chun Cao, Juan Feng, Hai-Long Tang, Mi-Mi Shu, Guang-Xun Gao, Xie-Qun Chen Department of Hematology, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi, People’s Republic of China Background: To evaluate the efficacy and safety of rituximab-based combination therapy for Waldenström macroglobulinemia (WM), we conducted this meta-analysis by pooling the rates of overall response, major response, complete response, and grade ≥3 hematological adverse events.Methods and materials: We searched for relevant studies in the databases of PubMed, Web of Science, Embase, and the Cochrane Library. The qualitative assessment of all the included articles was conducted with reference to the Newcastle–Ottawa Scale. A random-effects model was selected to perform all pooled analyses.Results: We identified altogether 22 studies with a total of 806 symptomatic WM patients enrolled. The pooled analysis indicated that the rituximab-based combination therapy achieved an overall response rate (ORR) of 84% (95% CI: 81%–87%), a major response rate (MRR) of 71% (95% CI: 66%–75%), and a complete response rate (CRR) of 7% (95% CI: 5%–10%). Rituximab plus conventional alkylating agents–containing chemotherapy (subgroup A) yielded an ORR of 86% (95% CI: 81%–89%), an MRR of 74% (95% CI: 69%–79%), and a CRR of 8% (95% CI: 4%–14%). Rituximab plus purine analog (subgroup B) resulted in an ORR of 85% (95% CI: 79%–89%), an MRR of 74% (95% CI: 66%–81%), and a CRR of 9% (95% CI: 4%–15%). Rituximab plus proteasome inhibitor (subgroup C) resulted in an ORR of 86% (95% CI: 81%–90%), an MRR of 68% (95% CI: 58%–77%), and a CRR of 7% (95% CI: 3%–11%). Rituximab plus immunomodulatory drug (subgroup D) attained relatively lower response rates, with an ORR of 67% (95% CI: 51%–81%), an MRR of 56% (95% CI: 27%–83%), and a CRR of 5% (95% CI: 1%–12%). Common grade ≥3 hematological adverse events consisted of neutropenia (33%, 95% CI: 17%–52%), thrombocytopenia (7%, 95% CI: 3%–11%), and anemia (5%, 95% CI: 3%–9%).Conclusion: Rituximab in combination with an alkylating agent, purine analog, or proteasome inhibitor is highly effective with tolerable hematological toxicities for WM. Keywords: response rate, individualized therapy\n
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".