Impact of Minimal Residual Diseases Status and Depth of Response on Survival Outcomes in Blinatumomab-Treated Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
OBJECTIVES: Acute lymphoblastic leukemia (ALL) is a common hematological malignancy that occurs due to blockage of B-lymphocyte maturation at an early stage of development and differentiation. The Food and Drug Administration approved blinotumomab to manage relapsed/refractory ALL (R/R ALL). This review aimed to determine the comparative efficacy of blinatumomab in treating R/R ALL. METHODS: Two reviewers searched 3 electronic databases, PubMed, ScienceDirect, and CENTRAL, for all relevant articles published until July 2024. All the articles that met the inclusion criteria were included in the review. RESULTS: Four hundred thirty-seven articles were found from the electronic search; however, only 21 articles met the inclusion criteria. A pooled analysis of the outcomes found that blinatumomab resulted in an improvement in both the OS (HR: 0.65; 95% CI: 0.51, 0.82; P =0.0003) and the DFS (HR: 0.57; 95% CI: 0.41, 0.80; P =0.001). Further analysis showed that the CR rate and MRD response of ALL patients to blinatumomab was 51.6% (95% CI: 48.5%, 54.6%; P =0.319) and 64.6% (95% CI: 53.4%, 74.3%; P =0.011), respectively. The safety analysis indicated that the incidence of serious AEs was comparable in patients receiving blinotumomab and those receiving standard chemotherapy (OR: 1.34; 95% CI; 0.91, 1.97; P =0.14). CONCLUSIONS: The findings show that blinatumomab is superior to standard chemotherapy in improving the OS and DFS of patients with R/R ALL. Furthermore, it has a more favorable safety profile, making it an effective alternative to conventional chemotherapy for managing R/R ALL.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".