Are we choosing mobilization regimens for autologous stem cell transplantation in multiple myeloma wisely: A single center comparison of <scp>GCSF</scp>+/−plerixafor vs cyclophosphamide/<scp>GCSF</scp>+/−plerixafor
Bibliographic record
Abstract
BACKGROUND: Autologous stem cell transplantation (ASCT) is a standard consolidation treatment for eligible patients with multiple myeloma (MM). There is no standardized mobilization regimen for collection of CD34+ stem cells, which is crucial to the success of ASCT. Cyclophosphamide/GCSF is an effective regimen, although reported associated toxicities include risk of febrile neutropenia (FN). Since plerixafor was introduced in Canada, this mobilization agent has been increasingly used as needed with GCSF at Kingston Health Science Centre (KHSC), with elimination of cyclophosphamide. This single center, retrospective, quality improvement study evaluates mobilization and ASCT outcomes of MM patients who had undergone stem cell mobilization at KHSC with cyclophosphamide/GCSF+/-plerixafor without antibiotics, cyclophosphamide/GCSF+/-plerixafor with antibiotics, and GCSF+/-plerixafor without antibiotics. METHODS: A retrospective chart review was conducted evaluating 137 patients. The primary outcome measure was FN rates with mobilization. Balancing measures include CD34+ cell collected, plerixafor usage, days of apheresis and transplant outcomes. Chi-square, ANOVA, or Kruskal-Wallis methods were used to test statistical significance where appropriate. RESULTS: Our study noted a higher total and day one CD34+ count in the two groups utilizing cyclophosphamide in mobilization. All nine cases of FN occurred in these two groups (P < .05). Addition of antibiotics decreased, but did not eliminate risk of FN. There were no significant differences in the rate of plerixafor usage and number of apheresis days. Difference in transplant outcomes, including engraftment and transfusion support, were statistically but not clinically significant. A larger sample size may be needed to explore this fully. There was no significant difference in length of transplant hospital stay. CONCLUSION: The elimination of cyclophosphamide from mobilization regimens for MM appears to significantly reduce FN rates, without increasing balancing measures such as total number of apheresis days, plerixafor usage, duration of transplant hospitalization or mortality outcomes.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".