Prestorage leukoreduction and low‐temperature filtration reduce hemolysis of stored red cell concentrates
Bibliographic record
Abstract
BACKGROUND: Universal prestorage leukoreduction in Canada created the perception that stored red cells (RBCs) are more hemolyzed than their unfiltered predecessors. A pool-split design tested the effects of leukoreduction on hemolysis of stored RBCs. STUDY DESIGN AND METHODS: Two ABO-matched units were pooled, divided, and then processed into leukoreduced (LR) and nonleukoreduced (NLR) units with the Pall LT-WB or RC-PL systems and sampled during standard processing and storage for testing of sterility, counts, hemolysis, and osmotic fragility. RESULTS: Room temperature (RT) filtration of 10 pairs of LT-WB-LR and -NLR units showed significantly different percentage of hemolysis (0.39%) and osmotic fragility (0.643%) at 42 days. Cold-stored and -filtered units (2 days at 4 degrees C before processing) were less hemolyzed, but showed a similar proportional decrease of hemolysis in LR units (0.13% vs. 0.25% at 42 days). RBCs from RC-PL systems showed the lowest hemolysis although there was a filtration effect (0.05% vs. 0.12%, 42 days). Osmotic fragility paralleled hemolysis. Segment samples gave inaccurate results. Two-day prefiltration cold storage reduced hemolysis from 0.36 to 0.07 percent (42 days, p < 0.001). RT-LR hemolysis became significantly higher by Day 10 and 4 degrees C LR by Day 12. NLR units showed hemolysis by Day 7. LR units filtered cold were less hemolyzed (p < 0.05) than RT-LR but osmotic fragility was unchanged. CONCLUSIONS: LR-RBCs prepared by any of three methods (LT-WB, RT or cold; RC-PL), filtered at 4 degrees C, were less hemolyzed during storage than nonfiltered concentrates: 4 degrees C leukoreduction is beneficial for RBCs and does not cause hemolysis or enhance fragility.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".