Treatment of older adult or frail patients with multiple myeloma
Bibliographic record
Abstract
Older adults with multiple myeloma (MM) are a growing population, and personalizing treatment based on disease and health status is imperative. Similar to MM staging systems that provide disease-related prognostic information, myeloma-specific frailty tools can better identify subgroups at greatest risk for treatment-related toxicity and early treatment discontinuation, as well as predict overall survival. Several myeloma-specific validated tools are well studied. Although these fitness/frailty scores have shaped our understanding of the heterogeneity among older adults with myeloma, the application of such scores in treatment decision making (ie, transplant considerations, relapse) is an unmet need. Here we outline how to incorporate frailty assessments in the evaluation of older adults with MM in the clinical setting with consideration of other factors such as patient preferences, treatment risks/benefits, life expectancy, and disease biology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".