Young Myeloma Patients: A Systematic Review of Manifestations and Outcomes
Bibliographic record
Abstract
Multiple myeloma usually affects older adults. However, younger patients constitute a significant subset as approximately 10% of cases occur in subjects younger than 50 years old. Young patients, who are underrepresented in the literature, are diagnosed during their most productive years of life, urging the need for tailored treatment approaches. This literature review aims to report recent studies specifically addressing young patients with a focus on characteristics at diagnosis, cytogenetics, treatments, and outcomes. We searched PubMed for studies involving young patients with multiple myeloma ≤50 years old. The time span of our literature review search was from 1 January 2010 to 31 December 2022. Overall, 16 retrospective studies were analyzed for this review. Young patients with multiple myeloma tend to have less advanced disease, more frequent light chain subtypes, and survive longer compared to their older counterparts. However, available studies included a limited number of patients; the newest revised international staging system was not used to stratify patients, cytogenetics varied from one cohort to another, and most patients did not receive contemporary triplet/quadruplet treatments. This review emphasizes the need to perform contemporary, large-scale retrospective studies to improve knowledge regarding the presentation and outcomes of young myeloma patients in the era of modern treatments.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".