Cost-effectiveness of a Transplantation Strategy Compared to Melphalan and Prednisone in Younger Patients with Multiple Myeloma
Bibliographic record
Abstract
High dose chemotherapy with autologous stem cell transplantation (ASCT) improves outcomes in patients 65 years of age or less with multiple myeloma. Survival and costs in a cohort of 16 patients who received melphalan and prednisone as part of a clinical trial were compared with those of 36 patients referred to our centre for consideration of ASCT. In the transplant group, survival and costs were extrapolated to match the period of observation in the melphalan and prednisone group. Patient-specific and average costs were calculated from the perspective of the Ontario Ministry of Health. Costs and survival were varied by 50% in the sensitivity analysis. Transplantation improved life expectancy by 19.3 months with a cost difference of 30,517 Canadian dollars. The incremental cost-effectiveness of transplantation compared with melphalan and prednisone was 25,710 Canadian dollars per life-year gained when additional pamidronate and follow-up costs were considered. Discounting costs and survival at 3 and 5% did not result in important differences. The sensitivity analysis resulted in best and worse case scenarios for transplantation compared with melphalan and prednisone of 13,049 dollars and 63,954 dollars per life-year gained respectively. In comparison with melphalan and prednisone, ASCT appears to be cost-effective in patients 65 years old or younger with myeloma.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".