Feeling the burn: the significant burden of febrile nonhemolytic transfusion reactions
Bibliographic record
Abstract
BACKGROUND: Febrile nonhemolytic transfusion reactions (FNHTRs) are characterized by a post-transfusion temperature rise (of ≥ 1°C, to ≥ 38°C) or chills/rigors unrelated to the underlying condition. FNHTRs are provoked by inflammatory cytokines in the product or by host antileukocyte antibodies against residual donor leukocytes. FNHTRs are among the most commonly reported transfusion disturbances and are generally deemed nonserious events. However, their impact on patients and hospitals may be underestimated. STUDY DESIGN AND METHODS: A search through two hemovigilance databases identified all known possible-to-definite FNHTRs over 3 years (2013-2015) at four academic hospitals using prestorage leukoreduced components. FNHTRs were assessed for frequency by product (red blood cells [RBCs], platelets [PLTs], intravenous immunoglobulin), diagnostics (bedside, chest imaging, serology, microbiology), and management (medications, disposition change). The definition of FNHTR was derived from Canada's Transfusion-Transmitted Injuries Surveillance System. RESULTS: For 437 FNHTRs, the overall per-product rate across all sites was 0.24%, or 0.17% with RBCs alone and 0.25% with PLTs alone. One-third of patients had significant fevers (≥ 39.0°C or a rise by ≥ 2.0°C). Approximately one-quarter underwent chest imaging within 48 hours, and 79% had blood cultures. A hospital admission directly attributable to the FNHTR, to exclude other causes of fever, occurred in 15% of FNHTR outpatients. CONCLUSION: An analysis of FNHTRs reveals a substantial burden of postreaction clinical activity in addition to the disturbance itself. Efforts to avoid this adverse event may save resources, reduce patient distress, and encourage compliance with more restrictive transfusion strategies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".