Predictors of unsuccessful mobilization with granulocyte colony‐stimulating factor alone in patients undergoing autologous hematopoietic stem cell transplantation
Bibliographic record
Abstract
Mobilization of hematopoietic stem cells is achieved with hematopoietic growth factors with or without chemotherapy or other agents. Although studies comparing granulocyte colony-stimulating factor (G-CSF) alone to combined regimens demonstrate an increase in stem cell yield in the latter, mobilization with G-CSF alone is still effective and has been widely practiced. We conducted a retrospective cohort study of consecutive patients at our institution who underwent at least one mobilization attempt with G-CSF between January 2000 and December 2008 to identify the proportion of patients failing one or more mobilization attempts and the potential predictors of mobilization failure with this regime. Out of 293 patients, 251 (86.6%) were successfully mobilized and 244 (83.6%) underwent hematopoietic stem cell transplantation. Median yield was 3.55 × 10⁶ CD34⁺ cells/kg. On univariate analysis, mobilization success was influenced by degree of previous treatment and underlying diagnosis (P < 0.001 each) but not by age (P = 0.114), sex (P = 0.860), or radiotherapy (P = 0.454). A diagnosis of non-Hodgkin's lymphoma (NHL) and number of previous chemotherapy regimens were predictors of failure on multivariate analysis. CD34⁺ yield was influenced by diagnosis and previous chemotherapy (P < 0.001 each). Mobilization with G-CSF alone yields adequate collections for most patients; however, heavily pretreated NHL patients with one failed attempt had high rates of remobilization failure and should be considered for alternative regimens.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".