Risk Stratification, Treatment Selection, and Transplant Eligibility in Multiple Myeloma: A Qualitative Study of the Perspectives and Self-Reported Practices of Oncologists
Bibliographic record
Abstract
BACKGROUND: Since the early 2000s, treatment options for multiple myeloma have rapidly expanded, adding significant complexity to the management of this disease. To our knowledge, no systematic qualitative research on clinical decision-making in multiple myeloma has been published. We sought to characterize how physicians view and implement guidelines and incorporate novel approaches into patient care. METHODS: We designed a semi-structured qualitative interview guide informed by literature review and an expert advisory panel. We conducted 60-minute interviews with a diverse sample of oncology physicians in the southeast United States. We used a constant comparative method to code and analyze interview transcripts. The research team and advisory panel discussed and validated emergent themes. RESULTS: Participants were 13 oncologists representing 5 academic and 4 community practices. Academic physicians reported using formal risk-stratification schemas; community physicians typically did not. Physicians also described differences in eligibility criteria for transplantation; community physicians emphasized distance, social support, and psychosocial capacity in making decisions about transplantation referral; the academic physicians reported using more specific clinical criteria. All physicians reported using a maintenance strategy both for post-transplant and for transplant-ineligible patients; however, determining the timing of maintenance therapy initiation and the response were reported as challenging, as was recognition or definition of relapse, especially in terms of when treatment re-initiation is indicated. CONCLUSIONS: Practices reported by both academic and community physicians suggest opportunities for interventions to improve patient care and outcomes through optimal multiple myeloma management and therapy selection. Community physicians in particular might benefit from targeted education interventions about risk stratification, transplant eligibility, and novel therapies.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".