Automated closed volume reduction process for apheresis stem cell grafts: From development to clinical implementation
Bibliographic record
Abstract
Abstract Background Collection of HPC by apheresis (HPC‐A) can sometimes result in higher collection volumes, increasing the dimethyl sulfoxide (DMSO) volume infused into patients and the space requirements in liquid nitrogen freezers. Volume reduction prior to the addition of cryoprotectant is an efficient means to reduce the DMSO load infused into patients and to optimize freezer storage space. Study Design and Methods To implement a closed semi‐automated volume reduction process, a method was developed to produce leukocyte‐rich mock apheresis products using buffy coats derived from whole blood collections. The mock HPC products were then used to measure the efficiency and reliability of the semi‐automated process over a range of volumes and cell concentrations. The resulting data was used to support the implementation of the process with concurrent monitoring. Results A closed, semi‐automated volume reduction process resulted in recoveries of over 93% and 91% of white blood cells and CD34+ cells with no significant loss of product viability or potency. Mean doses of CD34+ and CFU infused per kilogram recipient body weight were 4.0 ± 1.1 × 106/kg and 4.2 ± 1.7 × 105/kg, resulting in no delays in median time to neutrophil and platelet engraftment, significant increase in adverse reaction or nonconformances. Discussion The effectiveness outcomes of the first Canadian experience in the implementation of a closed semi‐automated volume reduction system in the processing of HPC‐A products for autologous transplant have met the predetermined acceptance criteria, supporting its use in a stem cell manufacturing laboratory compliant with good manufacturing practice regulations.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".