Whole blood treated with riboflavin and ultraviolet light: quality assessment of all blood components produced by the buffy coat method
Bibliographic record
Abstract
BACKGROUND: Pathogen inactivation (PI) technologies are currently licensed for use with platelet (PLT) and plasma components. Treatment of whole blood (WB) would be of benefit to the blood banking community by saving time and costs compared to individual component treatment. However, no paired, pool-and-split study directly assessing the impact of WB PI on the subsequently produced components has yet been reported. STUDY DESIGN AND METHODS: In a "pool-and-split" study, WB either was treated with riboflavin and ultraviolet (UV) light or was kept untreated as control. The buffy coat (BC) method produced plasma, PLT, and red blood cell (RBC) components. PLT units arising from the untreated WB study arm were treated with riboflavin and UV light on day of production and compared to PLT concentrates (PCs) produced from the treated WB units. A panel of common in vitro variables for the three types of components was used to monitor quality throughout their respective storage periods. RESULTS: PCs derived from the WB PI treatment were of significantly better quality than treated PLT components for most variables. RBCs produced from the WB treatment deteriorated earlier during storage than untreated units. Plasma components showed a 3% to 44% loss in activity for several clotting factors. CONCLUSION: Treatment of WB with riboflavin and UV before production of components by the BC method shows a negative impact on all three blood components. PLT units produced from PI-treated WB exhibited less damage compared to PLT component treatment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".