BLOOD COMPONENTS: Red blood cell hemolysis during blood bank storage: using national quality management data to answer basic scientific questions
Bibliographic record
Abstract
BACKGROUND: Hemolysis of red blood cells (RBCs) during blood bank storage is the most obvious manifestation of RBC storage system failure. However, its analysis is made difficult because the largest source of interunit difference is donor specific. Availability of data from national blood systems on large numbers of RBC units used for internal quality control (QC) purposes and stored and processed in uniform ways permits statistical analysis. STUDY DESIGN AND METHODS: Measures of hemolysis during and at the end of storage on randomly selected donor units observed for QC purposes were obtained from four national blood systems. Groups of these measures from units that had undergone similar processing and storage were sorted to create histograms and the histograms were compared statistically. RESULTS: A total of 14,087 measures were obtained under seven storage conditions, including more than 12,000 measures made in a single country under four closely related conditions. Distributions of percent hemolysis are skewed normal and outliers are random. Additive solutions appear to be equivalent, except that the 42 mmol/L mannitol in AS-1 reduces hemolysis compared to conventional 30 mmol/L mannitol in saline, adenine, glucose, and mannitol. Increasing storage from 35 to 42 days increased measured hemolysis by 30% and leukoreduction decreased it by 53%. CONCLUSIONS: Large national data sets provide useful information about the distribution of hemolysis at the end of RBC storage. This information can aid blood storage system development and regulatory science.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".