Autologous bone marrow transplant in standard-risk newly diagnosed multiple myeloma: A systematic review.
Bibliographic record
Abstract
174 Background: Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Bone marrow transplants in high-risk multiple myeloma have been a standard of care for newly diagnosed multiple myeloma (NDMM). Autologous bone marrow transplant (ABMT) has emerged as a promising therapeutic approach for standard risk of NDMM. We aim to review the existing literature on the efficacy of ABMT in standard-risk NDMM in comparison with non-transplant therapeutic approaches. Methods: We conducted a systematic search across PubMed, Google Scholar, Science Direct, and Embase for studies within the last 10 years. We included studies that compared ABMT with other therapies without any history of prior transplants in NDMM patients. We excluded studies that retrospective and case studies. We extracted firstly using PICO: standards risk NDMM, ABMT upfront therapy, progression-free survival, and toxicity. The quality of the study was assessed using the Newcastle-Ottawa scale or JADAD scale questionnaire. Results: Our search yielded 7 studies, with a total of 3728 patients in 7 randomized controlled trial studies. The endpoint was mainly progression-free survival (PFS), with others being response rates and stringent complete response (sCR). All studies consistently showed that ABMT yielded significantly better PFS and response rates in NDMM, with high-dose melphalan being the most common induction regime. ABMT resulted in relatively more severe toxic side effects compared to drugs only. However, the safety profile of ABMT is considered favorable, with manageable adverse events. Induction methods before ABMT include lenalidomide, bortezomib, dexamethasone, carfilzomib, and melphalan, with some studies reporting follow-ups on further maintenance therapy, mainly with lenalidomide. The effectivity of ABMT remains consistent regardless of the drugs used. Conclusions: ABMT is a favorable therapeutic approach for standard risk NDMM with manageable adverse effects and an acceptable safety profile. The effectivity of ABMT is regardless of the concomitant drug used.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".