Patient Preferences on Clinical Decision Making in Multiple Myeloma
Bibliographic record
Abstract
PURPOSE: To better understand the priorities that guide patients with multiple myeloma, we surveyed patients on four different treatment scenarios, each of treatment strategies shown to improve progression-free survival (PFS) but offering similar overall survival (OS) outcomes. METHODS: We conducted a survey using the HealthTree Cure Hub by the HealthTree Foundation, the largest online portal for people with plasma cell dyscrasias. RESULTS: The primary analysis cohort included 466 participants with myeloma, while an additional 297 responses from patients with smoldering myeloma or monoclonal gammopathy of uncertain significance were analyzed separately. When presented with either three-drug or four-drug frontline treatment for their myeloma, where four drugs offered better PFS, similar OS, and slightly increased toxicity, 56% of participants chose four drugs. For one-off consolidation treatment after induction, analogous to autologous transplant, which improved PFS but not OS, 50% of participants chose the consolidation. For maintenance therapy, where maintenance with two drugs offered better PFS, but similar OS and increased toxicity than one drug, 17% of participants chose two-drug maintenance. When evaluating a scenario for multiply relapsed disease, where a treatment improved PFS with increased toxicity, and no impact on OS, 7% of participants elected to receive this treatment. CONCLUSION: Our findings show that many patients choose not to receive treatments that improve PFS if they do not positively affect OS and lead to substantial clinical, financial, and/or time toxicities.
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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 | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".