Deglycerolization of manually glycerolized, frozen red cell concentrates using a closed system cell processor
Bibliographic record
Abstract
BACKGROUND: Historically, red cell concentrates (RCCs) have been manually glycerolized and deglycerolized using an open system (COBE 2991, Terumo). Implementation of a closed system cell processor (ACP-215, Haemonetics) for glycerolization and deglycerolization of RCCs creates a challenge for management of the historic cryopreserved RCC inventory. A study was undertaken to determine whether manually glycerolized frozen RCCs could be deglycerolized using the closed system processor, as the open system processors are being discontinued. STUDY DESIGN AND METHODS: Thirteen ABO/Rh matched RCCs were pooled and split to produce six large (approximately 354 mL) and six small (approximately 244 mL) RCCs. All units were stored for 14 days post-collection, manually glycerolized and frozen at ≤ -65°C for ≥72 h. Half of the units of each size were deglycerolized using the COBE 2991 and resuspended in 0.9% saline, and the remaining units were centrifuged, deglycerolized on the ACP-215, and resuspended AS-3. RBC quality was tested at 24 ± 2 h post-deglycerolization. RESULTS: All units deglycerolized on the ACP-215 had significantly lower hemolysis (p < .001) levels than those processed on the COBE2991. Large ACP-215 deglycerolized units had lower hematocrits (p < .05), hemoglobin (p < .01), and recovery (p = .001) than did large units deglycerolized on the COBE 2991. All ACP-215 units met the regulatory standards for hemolysis, hematocrit, hemoglobin, and recovery. DISCUSSION: The closed-system ACP-215 processor significantly reduced post-deglycerolization hemolysis in all units, and hemoglobin content in large units. The ACP-215, in combination with a centrifugation step, is suitable for processing cryopreserved RCCs that have been manually glycerolized.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".