Assessing the influence of component processing and donor characteristics on quality of red cell concentrates using quality control data
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Quality control (QC) data collected by blood services are used to monitor production and to ensure compliance with regulatory standards. We demonstrate how analysis of quality control data can be used to highlight the sources of variability within red cell concentrates (RCCs). MATERIALS AND METHODS: We merged Canadian Blood Services QC data with manufacturing and donor records for 28 227 RCC between June 2011 and October 2014. Units were categorized based on processing method, bag manufacturer, donor age and donor sex, then assessed based on product characteristics: haemolysis and haemoglobin levels, unit volume, leucocyte count and haematocrit. RESULTS: Buffy-coat method (top/bottom)-processed units exhibited lower haemolysis than units processed using the whole-blood filtration method (top/top). Units from female donors exhibited lower haemolysis than male donations. Processing method influenced unit volume and the ratio of additive solution to residual plasma. CONCLUSIONS: Stored red blood cell characteristics are influenced by prestorage processing and donor factors. Understanding the relationship between processing, donors and RCC quality will help blood services to ensure the safety of transfused products.
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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 | 0.026 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".