The impact of multiple myeloma induction therapy on hematopoietic stem cell mobilization and collection: 25-year experience
Bibliographic record
Abstract
While first-line induction therapy for patients with multiple myeloma has changed over the years, autologous hematopoietic stem cell transplantation still plays a significant role, improving both depth of response and progression-free survival of myeloma patients. Our 25-year experience in mobilizing hematopoietic stem and progenitor cells for 472 transplant-eligible myeloma patients was retrospectively reviewed. Patients were stratified according to the remission induction therapy received, and the outcomes were compared among the cohorts that received vincristine, adriamycin and dexamethasone (VAD) (n = 232), bortezomib and dexamethasone (BD) (n = 86), cyclophosphamide, bortezomib and dexamethasone (CyBorD) (n = 82) and other regimens (n = 67). Cyclophosphamide plus granulocyte colony-stimulating factor was the predominant mobilization regimen given. A greater number of CD34+ cells (9.9 × 10E6/kg, p = 0.026) was collected with less hospital admissions in BD patients (13%, p = 0.001), when compared to those receiving VAD (7.5 × 10E6/kg, 29%), CyBorD (7.6 × 10E6/kg, 19%), or other regimens (7.9 × 10E6/kg, 36%). Induction therapy did not influence the overall rate of unscheduled visits or the length of hospitalization because of complications following mobilization. The myeloma response was not significantly deepened following the cyclophosphamide administered for mobilization. This analysis demonstrates the importance of monitoring the impact of initial treatment on downstream procedures such as stem cell mobilization and collection.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".