Effect of premedication guidelines and leukoreduction on the rate of febrile nonhaemolytic platelet transfusion reactions
Bibliographic record
Abstract
Platelet transfusion reactions were prospectively studied in haematology/oncology patients at five university teaching hospitals over three consecutive summers. The initial summer study provided baseline information on the use of premedications and the rate of platelet transfusion reactions (fever, chills, rigors and hives). Most (73%) platelet recipients were premedicated and 30% (95% CI 28-33%) of transfusions were complicated by reactions. The second study followed implementation of guidelines for premedicating platelet transfusions. Despite a marked reduction in premedication (50%), there was little change in the platelet transfusion reaction rate, 26% (95% CI 24-29%), or the type of reactions. The third study followed implementation of prestorage platelet leukoreduction while maintaining the premedication guidelines. The reaction rate decreased to 19% (95% CI 17-22%). For nonleukoreduced platelets, there was a statistically significant association between the platelet age and reaction rate (P = 0.04). For leukoreduced platelets, there was no statistically significant association between platelet age and reaction rate (P = 0.5). Plasma reduction of nonleukoreduced platelet products also reduced the reaction rate. These prospective studies document a high rate of platelet transfusion reactions in haematology/oncology patients and indicate premedication use can be reduced without increasing the reaction rate. Prestorage leukoreduction and/or plasma reduction of platelet products reduces but does not eliminate febrile nonhemolytic platelet transfusion reactions.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".