The effect of processing method on the <i>in vitro</i> characteristics of red blood cell products
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: While the clinical impact of differences in red blood cell (RBC) component processing methods is unknown, there are concerns they may be confounding variables in studies such as the ongoing 'age of blood' investigations. Here, we compare the in vitro characteristics of red cell concentrates (RCCs) produced by several different processing methods. MATERIALS AND METHODS: Nine processing methods were examined: three apheresis methods (Alyx, MCS+ and Trima), as well as leucoreduced whole blood-derived RCCs produced by buffy coat and whole blood filtration and non-leucoreduced RCCs. RCCs were stored in saline-adenine-glucose-mannitol or additive solutions (AS) 1 or 3 for 42 days, with quality tested on day 5 and day 42. RESULTS: Many significant product differences were observed both early in and at the end of storage. Mean haemoglobin (Hb) ranged from 52 to 71 g/unit and mean Hct from 59·5 to 64·8%. Most RCC passed regulated quality control criteria according to Canadian Standards Association guidelines, although there were some failures relating to Hb content and residual WBC counts. CONCLUSION: Processing method impacts RCC characteristics throughout storage; better understanding of these differences and reporting of processing method details is critical.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".