Treatment of elderly and frail myeloma patients
Bibliographic record
Abstract
Multiple myeloma (MM) is an incurable cancer of older adults. Given the aging population, the prevalence of older adults with MM is expected to further increase over the next decade. Challenges in treating older adults result from the heterogeneity of both aging itself and the disease. Over the past two decades, tremendous progress has been made in improving the outcome in this age group with novel therapeutics, including immunomodulatory drugs, proteasome inhibitors, and more recently anti-CD38 monoclonal antibodies, becoming an integral part of initial treatment. Further improvements are expected over the next decade with novel immunotherapy, including T-cell engagers and chimeric antigen receptor therapies. With additional novel treatments, assessment of patient frailty will become increasingly important in balancing the optimal treatment of patients. In this review, we focus on the treatment of elderly and frail older adults with MM. The first part of our review will focus on pertinent investigations, considerations for treatment initiation and initial risk stratification, including frailty assessment prior to treatment initiation. In the second part, we will focus on the overall goals of treatment and therapeutic options for newly diagnosed and those with relapsed/refractory MM, including novel immunotherapy and supportive care. Lastly, we will end this review by highlighting current knowledge gaps and providing suggestions for future directions to further improve outcomes among older adults with MM.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".