Impact of Granulocyte Colony-Stimulating Factor (G-CSF) on Clinical Outcomes in Allogeneic Hematopoietic Cell Transplantation: Does Speeding Up Neutrophil Engraftment Make a Difference?
Bibliographic record
Abstract
Background. Despite decades of post–allogeneic hematopoietic cell transplantation (HCT) growth factor utilization, its role remains undefined, leading to ongoing debates and research. The theoretical impacts of growth factors have been challenged in numerous studies. Methods. In this retrospective cohort study conducted at the Princess Margaret Cancer Centre, we analyzed the clinical outcomes of 509 patients who underwent allogeneic HCT between May 1, 2019, and May 31, 2022. This study aimed to assess the impact of granulocyte colony-stimulating factor (G-CSF) administration posttransplantation on neutrophil and platelet engraftment, incidence of bloodstream infections (BSIs), graft-versus-host disease, engraftment syndrome (ES), and survival metrics including overall survival, nonrelapse mortality, and graft-versus-host disease–free/relapse-free survival. Results. Our findings indicate that G-CSF administration expedited neutrophil engraftment (16 versus 18 d, P = 0.009) and was associated with a decreased incidence of BSI (9.4% versus 31.3%, P = 0.014). However, this benefit was counterbalanced by a significant delay in platelet engraftment (21 versus 17 d, P < 0.001). Multivariate logistic regression analysis identified mismatched donors (odds ratio, 1.72; 95% confidence interval, 1.03-2.88; P = 0.038) and the duration of G-CSF therapy (odds ratio, 1.04; 95% confidence interval, 1.00-1.09; P = 0.038) as independent predictors for the development of ES. Despite these hematological impacts, there was no observed advantage in overall survival, nonrelapse mortality, or graft-versus-host disease–free/relapse-free survival among patients who received G-CSF compared with those who did not. Conclusions. Although G-CSF post-HCT expedited neutrophil engraftment and reduced BSI risk, it did not result in a survival advantage. The association with ES necessitates careful consideration.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".