Recent Advances in the Management of Smoldering Multiple Myeloma
Bibliographic record
Abstract
There is remarkable progress in the treatment of multiple myeloma (MM) with significant improvement in survival in the past 10 years. Monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM) can evolve into symptomatic multiple myeloma (sy-MM) with organ involvement. SMM has associated with a much higher progression to MM compared to MGUS. In 2014, International Myeloma Working Group (IMWG) reclassified ultra-high-risk smoldering myeloma patients with bone marrow plasma cells > 60% or serum-free light chain ratio (FLCr) > 100 or > 1 focal bone lesion on the magnetic resonance imaging as MM. SMM is a heterogeneous disorder with probability for progression to myeloma up to 50% in the first 5 years. Several risk models and clinical features have been identified to stratify the risk of progression to MM. Thanks to advances in our understanding of the genomic profile of MM, there are several ongoing clinical trials, and genomic studies are being done to assess the risk of progression to MM and early intervention. There is still no standard criterion regarding when to start therapy. This review discusses identifying SMM patients who are at high risk of progression to sy-MM and recent development of new and early treatment strategies and ongoing clinical trials for these high-risk SMM patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".