Determining the Volume of Additive Solution and Residual Plasma in Whole Blood Filtered and Buffy Coat Processed Red Cell Concentrates
Bibliographic record
Abstract
BACKGROUND: Residual plasma in transfused red cell concentrates (RCCs) has been associated with adverse transfusion outcomes. Despite this, there is no consensus on the standard procedure for measuring residual plasma volume. METHODS: The volumes of residual plasma and additive solution were measured in RCCs processed using two separation methods: whole blood filtration (WBF) and buffy coat (BC)/RCC filtration. The concentration of mannitol and albumin in RCC components was measured using colorimetric assays. Mannitol concentration was used to calculate additive solution volume. Residual plasma volume was calculated using two methods. RESULTS: Calculated RCC supernatant volumes were much lower in BC-processed components compared to WBF-processed components (BC = 97 ± 6 ml, WBF = 109 ± 4 ml; p < 0.05). Calculated additive solution volumes were greater in WBF- than in BC-processed components (BC = 81 ± 4 ml, WBF = 105 ± 2 ml; p < 0.05). Absolute residual plasma volume varied significantly based on the calculation method used. CONCLUSION: Disparity between plasma volume calculation methods was observed. Efforts should be made to standardize residual plasma volume measurement methods in order to accurately assess the impact of residual plasma on transfusion outcomes.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".