The efficacy and safety of anti-CD20 antibody for the treatment of B-ALL: a systematic review and meta-analysis
Bibliographic record
Abstract
Several clinical trials with anti-CD20 antibodies have successfully treated Acute Lymphoblastic Leukemia. Nevertheless, systematic comparisons between different anti-CD20 antibody trials are rare, and a comprehensive evaluation of their efficacy and safety has yet to be performed. The purpose of this systematic review and meta-analysis was to assess the efficacy and safety of anti-CD20 antibodies in the treatment of acute lymphoblastic leukemia and to guide clinical decision-making regarding the use of anti-CD20 antibody therapy. According to the PRISMA guidelines, Embase, Cochrane Library, PubMed, Web of Science, and ClinicalTrials.gov were searched for clinical trials conducted up to November 1, 2024, for the evaluation of anti-CD20 antibodies (rituximab, obinutuzumab, and ofatumumab) and corresponding controls. After screening the literature and extracting data, study quality was assessed using the Cochrane ROB 2 tool (RCTs) and the Newcastle-Ottawa Scale (cohorts). Heterogeneity was assessed using the I² test. Based on the results of the heterogeneity test, meta-analysis was performed in RevMan 5.4 software with either a random-effects model or a fixed-effects model. We combined data from eight studies ( n = 1330 patients, including two RCTs and six cohorts). Meta-analysis showed that anti-CD20 monoclonal antibodies significantly improved overall survival (OR = 1.89, 95% CI: 1.21–2.95, p = 0.005) and event-free survival [OR = 1.68, 95% CI: 1.32–2.14, p < 0.0001] after >1-year follow-up, and increased complete remission rates ( p < 0.05). No significant differences were observed in common adverse events between groups. Subgroup analyses by study type did not alter these conclusions. Overall, anti-CD20 antibody therapy was more efficacious than the corresponding control and did not increase the incidence of grade 3–4 adverse events. Ofatumumab may be a more effective anti-CD20 antibody for the treatment of ALL.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".