Segments from red blood cell units should not be used for quality testing
Bibliographic record
Abstract
BACKGROUND: Nondestructive testing of blood components could permit in-process quality control and reduce discards. Tubing segments, generated during red blood cell (RBC) component production, were tested to determine their suitability as a sample source for quality testing. STUDY DESIGN AND METHODS: Leukoreduced RBC components were produced from whole blood (WB) by two different methods: WB filtration and buffy coat (BC). Components and their corresponding segments were tested on Days 5 and 42 of hypothermic storage (HS) for spun hematocrit (Hct), hemoglobin (Hb) content, percentage hemolysis, hematologic indices, and adenosine triphosphate concentration to determine whether segment quality represents unit quality. RESULTS: Segment samples overestimated hemolysis on Days 5 and 42 of HS in both BC- and WB filtration-produced RBCs (p < 0.001 for all). Hct and Hb levels in the segments were also significantly different from the units at both time points for both production methods (p < 0.001 for all). Indeed, for all variables tested different results were obtained from segment and unit samples, and these differences were not consistent across production methods. CONCLUSION: The quality of samples from tubing segments is not representative of the quality of the corresponding RBC unit. Segments are not suitable surrogates with which to assess RBC quality.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".