Transfusion-Related Acute Lung Injury in Patients With Burns
Bibliographic record
Abstract
Transfusion-related acute lung injury (TRALI) has not been systematically described in patients with burn injury, and the characterization of TRALI in patients with pre-existing acute lung injury (ALI) or acute respiratory distress syndrome (ARDS) also is lacking. Our aim was to identify TRALI in burn patients and to attempt to characterize transfusion (TXN)-related pulmonary deterioration in burn patients with pre-existing ALI or ARDS. We undertook a retrospective review of mechanically ventilated and transfused burn patients at an adult regional burn center between January 1, 2003, and January 5, 2005. A blinded intensivist independently rated pre- and post-TXN chest radiographs (CXRs). There were 25 patients (age 51 +/- 19 years, %TBSA burns 30 +/- 19, full thickness %BSA 17 +/- 19, with a 24% incidence of smoke inhalation) who received 124 TXNs. New ALI developed within 6 hours after four TXNs. In one TXN, there were no other precipitating causes (eg, infection, inhalation injury), suggesting possible TRALI (incidence 0.8%). Existing ALI or ARDS was present before 63 (51%) of the TXNs. Definite worsening of the CXR and deterioration in the PaO2/FiO2 ratio (18% +/- 4%) within 6 hours of TXN occurred after six transfusions. In two of the TXNs, there were no other precipitating causes, suggesting possible TXN-related pulmonary deterioration (incidence 3.2%). Vigilance must be maintained for TRALI in burn patients. For patients with existing ALI or ARDS, we suggest that worsening of the CXR and reduction in the PaO2/FiO2 ratio by 20% or more within 6 hours of transfusion should be investigated for possible TRALI with appropriate donor investigations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".