Multiple Myeloma in the Very Old: An IASIA Conference Report
Bibliographic record
Abstract
Multiple myeloma (MM) in patients aged greater than 80 years poses an increasingly common challenge for oncology providers. A multidisciplinary workshop was held in which MM-focused hematologists/oncologists, geriatricians, and associated health-care team members discussed the state of research for MM therapy, as well as themes from geriatric medicine that pertain directly to this patient population. A summary statement of our discussions is presented here, in which we highlight several topics. MM disproportionately affects senior adults, and demographic trends indicate that this trend will accelerate. Complex issues impact cancer in seniors, and although factors such as social environment, comorbidities, and frailty have been well characterized in nononcological geriatric medicine, these themes have been inadequately explored in cancers such as MM, despite their clear relevance to this field. Therapeutically, novel agents have improved survival for MM patients of all ages, but less so for seniors than younger patients for a variety of reasons. Lastly, both MM- and treatment-related symptoms and toxicities require special attention in senior adults. Existing research provides limited insight into how best to manage these often complex patients, who are often not reflected in typical clinical trial populations. We hence offer suggestions for clinical trials that address knowledge gaps in how to manage very old and/or frail patients with MM, given the complicated issues that often surround this patient population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".