Second Autologous Stem Cell Transplantation as Salvage Therapy for Multiple Myeloma: Impact on Progression-Free and Overall Survival
Bibliographic record
Abstract
The role of a second autologous stem cell transplant (ASCT) as salvage therapy is unclear, particularly with the availability of novel agents to treat progressive multiple myeloma (MM). We retrospectively reviewed all MM patients who received a second ASCT as salvage therapy at our center from March 1992 to December 2009. Eighty-one MM patients received a second ASCT for relapsed MM. The median time to relapse after first transplant was 39 months (9.83-100). All patients received reinduction therapy before the second ASCT. The high-dose regimen given before the second ASCT consisted of melphalan (MEL) alone in the majority. Complete response, very good partial response, and partial response were seen in 7.7%, 39.7%, and 50%, respectively, at day 100 post-ASCT; the median time to relapse after the second ASCT was 19 months. Early deaths occurred in 2.6%. Median progression-free survival (PFS) based on the time to myeloma relapse after first ASCT was 9.83 months (relapse ≤ 24 months) and 17.3 months (relapse ≥ 24 months) (P < .05). Median overall survival (OS) was 28.47 months (relapse ≤ 24 months) and 71.3 months (relapse >24 months) (P = .006). Second ASCT is a feasible and safe option for salvage therapy in MM. The best outcome was observed in patients whose time to progression was >24 months after first ASCT, as these patients had a subsequent PFS lasting over 1 year and an OS of almost 6 years.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".