Assessing the influence of component processing and donor characteristics on quality of red cell concentrates using quality control 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
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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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 | 0.002 | 0.001 |
| 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 | 0.002 |
| 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