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Bibliographic record
Abstract
Background: Buffy coat method is one of the blood components processing methods widely used in many countries including Europe and Canada. For the first time in Korea, we evaluated the qualities of blood components manufactured by buffy coat method. Methods: We collected 400 mL whole bloods using the quadruple top and bottom blood bag from thirty-five donors. Whole bloods were processed into leukoreduced RBC, leukoreduced pooled platelet, and 24 hr frozen plasma by the buffy coat method with blood bags and instruments of Fenwal and Fresenius. The qualities of each blood component were analyzed at each scheduled day, and compared with the standard guidelines of quality control in Korean Red Cross. Results: The volume and hemoglobin of RBCs were lower than the standard guidelines. Comparing with the standard of apheresis platelets, leukoreduced pooled platelets showed higher total platelet yield with the median 3.70횞1011/unit. Frozen plasma showed increased volume recovery than the standard guideline, but the activity of factor VIII at Day 35 was decreased to 0.66짹0.14 IU/mL. Conclusion: We have found that the yields of pooled platelet and the frozen plasma processed by buffy coat method were higher than the standard guidelines. To introduce the buffy coat method to routine blood component separation process in Korea, further evaluations about the cost-effectiveness of buffy coat method are required.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".