Not all red cell concentrate units are equivalent: international survey of processing and in vitro quality data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: In vitro qualitative differences exist in red cell concentrates (RCCs) units processed from whole blood (WB) depending on the method of processing. Minimal literature exists on differences in processing and variability in quality data. Therefore, we collected information from blood manufacturers worldwide regarding (1) details of WB collection and processing used to produce RCCs and (2) quality parameters and testing as part of routine quality programmes. METHODS: A secure web-based survey was developed, refined after pilot data collection and distributed to blood centres. Descriptive analyses were performed. RESULTS: Data from ten blood centres in nine countries were collected. Six blood centres (60%) processed RCCs using the top-and-top (TAT) method which produces RCCs and plasma, and eight centres (80%) used the bottom-and-top (BAT) which additionally produces buffy coat platelets. Five of the centres used both processing methods; however, four favoured BAT processing. One centre utilized the Reveos automated system exclusively. All centres performed pre-storage leucoreduction. Other parameters demonstrated variability, including active cooling at collection, length of hold before processing, donor haemoglobin limits, acceptable collection weights, collection sets, time to leucoreduction, centrifugation speeds, extraction devices and maximum RCC shelf life. Quality marker testing also differed amongst blood centres. Trends towards higher RCC unit volume, haemolysis and residual leucoctyes were seen in the TAT compared with BAT processing across centres. CONCLUSION: Methods and parameters of WB processing and quality testing of RCCs differ amongst surveyed blood manufacturers. Further studies are needed to assess variations and to potentially improve methods and product quality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it