The effect of prestorage WBC reduction on the rates of febrile nonhemolytic transfusion reactions to platelet concentrates and RBC
Bibliographic record
Abstract
BACKGROUND: Febrile non-hemolytic transfusion reactions (FNHTRs) are a common complication of platelet concentrate (PC) and RBC transfusions, usually ascribed to cytokines released by WBCs and perhaps the platelets themselves during storage. Prestorage WBC reduction should abrogate the accumulation of these cytokines reducing the number of FNHTRs. STUDY DESIGN AND METHODS: A retrospective analysis of FNHTR to PCs and RBCs before universal WBC reduction (PrUR) (July 1997-January 1998 for PCs, July 1997-July 1999 for RBCs) and after its introduction (PoUR) (February 1998-August 2001 for PC, August 1999-August 2001 for RBCs) was undertaken. All transfusion reactions were stratified based on component and date of reaction. Other adverse transfusion reactions were grouped into three periods: July 1997-January 1998, February 1998-July 1999, and August 1999-August 2001. A chi-square test was performed to determine the significance of the differences between groups. RESULTS: In the PRUR group, there were: 231 FNHTRs in 70,396 RBC units transfused (0.33%) and 29 FNHTRs in 6502 PC units transfused (0.45% percent). In the PoUR group, there were 136 FNHTRs in 72,949 RBC units transfused (0.19%, p < 0.001) and 56 FNHTRs in 50,555 PC units transfused (0.11%, p < 0.001). Of the other adverse events, only TRALI reactions were significantly reduced. CONCLUSION: Prestorage WBC reduction significantly reduced the rate of FNHTRs to PCs and RBCs.
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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 | 0.002 | 0.009 |
| 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) | 0.001 | 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".