Efficacy and Safety of Busulfan-Based Conditioning Regimens for Multiple Myeloma
Bibliographic record
Abstract
Multiple myeloma is a malignancy of B cells characterized by accumulation of abnormal plasma cells in the bone marrow. In the past 20 years, the use of high-dose therapies and novel agents has resulted in significant and meaningful improvements in survival. Autologous stem cell transplantation (auto-SCT) following a high-dose melphalan-conditioning regimen represents the standard of care for younger patients as well as older patients with a good performance status. A number of strategies have been proposed to improve the outcome of auto-SCTs, including the incorporation of new agents such as thalidomide, lenalidomide, and bortezomib into the induction regimen administered before auto-SCT; the administration of maintenance therapy after auto-SCT; the incorporation of novel agents into chemotherapeutic regimens after transplantation as consolidation therapy; and the use of reduced-intensity allogeneic transplantation after an initial autograft. Although these approaches have demonstrated some success in improving responses after auto-SCT, none of these strategies are curative. An additional strategy to improve outcomes after auto-SCT is to enhance the immediate pretransplant conditioning regimens by either increasing the dose of melphalan or by incorporating novel agents, such as busulfan. This literature review focuses on the efficacy and safety of busulfan-based conditioning regimens for auto-SCT in patients with multiple 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".