Economic burden of relapsed or refractory multiple myeloma: Results from an international trial
Bibliographic record
Abstract
OBJECTIVE: The direct cost of relapsed or refractory multiple myeloma (RRMM) is documented; indirect costs are being explored. Healthcare payers seek cost-offsets from therapies that improve clinical outcomes but challenge budgets; employers seek lower absenteeism and better productivity. Study goals were to: (i) identify direct and indirect economic factors of RRMM, and (ii) explore longitudinal relationships between clinical, economic, and health-related quality of life (HRQoL) assessments. METHODS: Economic questionnaire, clinical, and HRQoL data from a multisite, international, randomized, controlled study in RRMM were analyzed. RESULTS: Patients (n=263) were 53.6% male, 91.6% Caucasian; mean age of 62.9 years, median Eastern Cooperative Oncology Group status of 1 (56.3%). Moderate to severe pain or fatigue was reported by 30.4% and 70.6%, respectively. At baseline, ≥1 hospitalization was reported by 107 (41.8%); 182 (71.1%) and 86 (33.6%) reported specialist and family physician visits, respectively. A total of 28 (10.8%) were working: 10 (37.0%) of which reported RRMM-driven absenteeism ≥1 day. Of those who were not working, 110 (48.2%) indicated that it was due to RRMM. Multivariate modeling showed lower hospitalization with a major tumor response (β=-1.44, CI: -2.89 to 0.01, P=.05). CONCLUSIONS: Substantial RRMM indirect, social costs were observed. Better major tumor response may reduce hospital visits.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".