Acute transfusion reactions in the pediatric intensive care unit
Bibliographic record
Abstract
BACKGROUND: Acute transfusion reactions (ATRs) are probably underdiagnosed in critically ill children because associated symptoms can frequently be attributed to the patient's underlying disease. This study was undertaken to determine the incidence, type, imputability, and severity of ATRs observed in a tertiary care pediatric intensive care unit (PICU). STUDY DESIGN AND METHODS: All transfusions of labile blood product administered to consecutive patients admitted to our PICU, between February 2002 and February 2004, were prospectively recorded. For each transfusion, the bedside nurse recorded the patient's status before, during, and up to 4 hours after the transfusion, as well as the presence of any new sign or symptom suggesting an ATR. Three independent experts retrospectively reviewed all transfusion event reports and hospital charts. The presence, type, imputability, and severity of ATRs were adjudicated by consensus of two of three experts (Delphi method), with predefined criteria. RESULTS: A total of 2509 transfusions were administered to 305 patients during the study. Forty transfusion events (1.6%) were confirmed to be ATRs by expert consensus: 24 febrile nonhemolytic, 6 minor allergic, 4 isolated hypotension, 3 bacterial contamination, 1 major allergic (anaphylactic shock), 1 TRALI, and 1 hemolytic reaction. Imputability of ATRs was probable or possible in 35 cases (88%). ATRs led to an immediate vital threat in 15 percent of cases. CONCLUSION: Improved surveillance of transfusions given to PICU patients and better knowledge of these reactions by health care professionals should improve the safety of transfusions in the PICU.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".