The Role of Allogeneic Stem Cell Transplantation in Multiple Myeloma: A Systematic Review of the Literature
Bibliographic record
Abstract
Multiple myeloma (MM) is an indolent B-cell malignancy, where treatment is aimed at preventing organ dysfunction from light chain accumulation (slowing disease progression) and inducing remission. Allogeneic stem cell transplant (allo-SCT), through graft versus myeloma (GVM) effects, has the potential to induce remission to a potentially curative-like state. In this systematic review, we aimed to understand this relationship to the risks and severity of disease in categorized patients and gain an updated comprehension of the future of allo-SCT in MM treatment. We conducted this review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and searched the PubMed database to obtain the specified literature with both the use of keywords and Medical Subject Headings (MeSH). A total of 16 relevant articles were included for discussion after the quality appraisal was completed, as appropriate, by either the Cochrane tool or Newcastle-Ottawa checklist. Our review concludes that while allo-SCT may benefit high-risk patients, successful procedures may incorporate a tandem autologous hematopoietic stem cell transplant approach in combination with novel pharmacologic contributions for which there is an observed synergy in the modulation of the immunologic microenvironment. Furthermore, tailored patient selection by evaluating pre-transplant factors including high-risk cytogenetics, age, and pre-salvage International Staging System (ISS) can predict post-transplantation success including non-relapse mortality. Successive research should continue to revise and update treatment options as the evolving therapeutic drug regimens may change over the course of indolent disease.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".