44 Success of stem cell collection and neutrophil recovery in pediatric patients with brain tumours
Bibliographic record
Abstract
BACKGROUND: The use of high-dose chemotherapy (HDC) and autologous hematopoietic stem cell transplant (HSCT) has been used in certain pediatric patients with brain tumours to delay/spare radiotherapy. We aimed to study factors predicting a successful stem cell collection (SCC) and correlate stem cell dose infused with HSCT outcomes. METHODS: A retrospective chart review was undertaken for pediatric patients with brain tumours treated at our centre with HDC/HSCT between 2004-2016. RESULTS: Fifty-five patients were identified (32 male) with median age of 6.3 years at time of SCC (range 0.4-18.7). Patients' diagnoses were medulloblastoma (62%), ATRT (20%), and PNET (18%). Most patients (82%) underwent a single/1-day SCC, while the remaining required 2 SCC procedures. Peripheral blood stem cells were the source in most collections (95%). Successful SCC (CD34 collected greater-than-or-equal-to 2 x10^6/kg/transplant) and ideal SCC (greater-than-or-equal-to 5 x10^6/kg/transplant) was achieved in 85% and 45% of patients, respectively. Use of mobilizing chemotherapy with G-CSF was the only factor associated with achieving an ideal collection, while gender, age, stem cell source, and pre-apheresis peripheral blood CD34 count were not significant. Higher CD34/kg infused was associated with faster neutrophil engraftment in the first 3 courses of HDC/HSCT and platelet engraftment in the first course. CONCLUSIONS: The majority of SCC for autologous HSCT can be successfully completed with a single apheresis session. Mobilization with both chemotherapy and G-CSF yields higher CD34 compared to G-CSF alone. Higher dose of CD34/kg infused was associated with faster neutrophil and to a more limited scale platelet recovery post-HSCT.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".