From Development to Implementation: Adjusting the Hematocrit of Deglycerolized Red Cell Concentrates to Meet Regulatory Standards
Bibliographic record
Abstract
BACKGROUND: Before transfusion, thawed frozen red cell concentrates (RCCs) must be deglycerolized. In order to ensure that these products meet regulatory standards for hematocrit, an approach to manipulate hematocrit post deglycerolization was developed and implemented. METHODS: Glycerolized and frozen RCCs were thawed and deglycerolized using the COBE 2991 cell processor, and the final product's hematocrit was adjusted by addition of various volumes of 0.9% saline / 0.2% dextrose. The in vitro quality of RCCs (hematocrit, hemolysis, hemoglobin content, volume, recovery, ATP, supernatant potassium, and others) were compared to Canadian Standards Association (CSA) and other standards for deglycerolized RCCs. RESULTS: Addition of saline/dextrose re-suspension solution in a range of 65-90 g post deglycerolization led to acceptable hematocrits. In the pilot study, this approach resulted in RCCs meeting all CSA standards for deglycerolized RCCs, with stimulation of RBC metabolism demonstrated by increased ATP concentration. In the validation phase, results were similar, although the CSA hemolysis standard was not met. Pre- and post-implementation data confirmed that manipulated RCCs met CSA hematocrit standards. CONCLUSION: This process was implemented at Canadian Blood Services to provide deglycerolized RCCs that meet the CSA hematocrit standard. However, pre- and post-implementation data reveal that this deglycerolization process is not sufficient to have RCCs consistently meet hemolysis standards.
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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 | 0.017 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".