Optimizing autologous nonmobilized mononuclear cell collections for cellular therapy in pediatric patients with high‐risk leukemia
Bibliographic record
Abstract
BACKGROUND: The manufacturing of cellular products for immunotherapy, such as chimeric antigen receptor T cells, requires successful collection of mononuclear cells. Collections from children with high-risk leukemia present a challenge, especially because the established COBE Spectra apheresis device is being replaced by the novel Spectra Optia device (Optia) in many institutions. Published experience for mononuclear cell collections in children with Optia is lacking. Our aim was to compare the two collection devices and describe modified settings on the Optia to optimize mononuclear cell collections. STUDY DESIGN AND METHODS: As a quality initiative, we retrospectively collected and compared data from mononuclear cell collections on both devices. Collected data included patient's clinical characteristics; collection parameters, including precollection lymphocyte/CD3 counts, total blood volumes processed, runtimes, and side effects (including complete blood count and electrolyte changes); and product characteristics, including volumes and cell counts. Collection efficiencies and collection ratios were calculated. RESULTS: Twenty-six mononuclear cell collections were performed on 20 pediatric patients: 11 with COBE and 15 with Optia. Adequate mononuclear cell products were successfully collected with a single procedure from all patients except one, with mean calculated mononuclear cell collection efficiency that was significantly higher from Optia collections compared with COBE collections (57.9 ± 4.6% vs 40.3 ± 6.2%, respectively; p = 0.04). CD3-positive yields were comparable on both machines (p = 0.34) with significantly smaller blood volumes processed on Optia. Collected products had larger volumes on Optia. No significant side effects attributed to the procedure were noted. CONCLUSION: Mononuclear cell apheresis using the Optia device in children is more efficient and is as safe as that with the COBE device.
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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 | 0.003 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".