Universal prestorage leukoreduction in Canada decreases platelet alloimmunization and refractoriness
Bibliographic record
Abstract
Randomized controlled trials have shown a reduction in platelet alloimmunization and refractoriness in patients with acute leukemia (AL) with the use of poststorage leukoreduction of blood products. Universal prestorage leukoreduction (ULR) of red cell and platelet products has been performed in Canada since August 1999. We conducted a retrospective analysis of 13 902 platelet transfusions in 617 patients undergoing chemotherapy (CT) for AL or stem cell transplantation (SCT) before (n = 315) and after (n = 302) the introduction of ULR. Alloimmunization was significantly reduced (19% to 7%, P <.001) in the post-ULR group. Alloimmune platelet refractoriness was similarly reduced (14% to 4%, P <.001). Fewer patients in the post-ULR group received HLA-matched platelets (14% vs 5%, P <.001). Alloimmunization and alloimmune refractoriness in the 318 patients who were previously pregnant and/or transfused were also reduced after ULR (P =.023 and P =.005, respectively). In a Cox regression model, the 3 independent factors that predicted for alloimmune refractoriness were nonleukoreduced blood products (relative risk [RR], 2.2 [95% CI, 1.2-4.3]), a history of pregnancy and/or transfusion (RR, 2.3 [95% CI, 1.3-4.2]), and receipt of 13 or more platelet transfusions (RR, 6.0 [95% CI, 2.4-15.3]). In conclusion, ULR reduces alloimmunization, refractoriness, and requirements for HLA-matched platelets when applied as routine transfusion practice to patients receiving CT or SCT.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".