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 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 | 0.000 | 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) | 0.002 | 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".