Use of CD38 Antibodies in High-Risk Newly Diagnosed Multiple Myeloma: A Meta-Analysis
Bibliographic record
Abstract
Abstract The anti-CD38 monoclonal antibodies daratumumab and isatuximab are both approved by the United States Food and Drug Administration for use in patients with newly diagnosed multiple myeloma (NDMM) in both the transplant-eligible and transplant-ineligible settings. However, the degree to which high-risk patients in particular benefit from the addition of an anti-CD38 monoclonal antibody to NDMM regimens is not clear. We conducted a meta-analysis to evaluate the effect of anti-CD38 monoclonal antibodies on progression free survival (PFS) using published randomized clinical trials completed in newly diagnosed multiple myeloma patients using these drugs. A total of 9 randomized trials were identified and included in the analysis. Frontline receipt of an anti-CD38 monoclonal antibody was associated with improved PFS both in patients with standard-risk NDMM (HR 0.51; 95% CI 0.46-0.58) and high-risk NDMM (HR 0.64, 95% CI 0.49-0.84). Similarly, the benefit was seen in standard risk and high risk NDMM in both the transplant and non-transplant trials as well. Overall, CD38 antibodies show improvement in progression free survival in patients with both standard and high-risk multiple myeloma.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.031 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".