Bibliographic record
Abstract
Transfusion-related acute lung injury (TRALI) is a life-threatening complication of blood transfusion and has emerged as a leading cause of transfusion-associated morbidity and mortality. Prospective data of patients in the intensive care units have documented a significantly higher incidence of TRALI than previously reported. TRALI is likely to be under diagnosed due to a lack of awareness and understanding of this clinical entity, and because of the clinical presentation mirroring acute respiratory distress syndrome with its various potential aetiologies. We present a case of fatal TRALI in a 61-year-old man, who exhibited acute onset of respiratory distress postoperatively in association with transfusion of fresh frozen plasma (FFP) containing human leukocyte antigen (HLA) antibodies reactive with patient HLA. The patient had just been pneumonectomied. Risk factors of the blood component as well as patient specific risk factors determining the risk of TRALI are discussed. There is a need to increase the awareness and reporting of this diagnosis, in part to promptly institute the appropriate treatment and in part to identify and defer donors implicated in TRALI. Furthermore, awareness of this type of complication to transfusion of blood products could increase rational use of blood products, including minimising the use of FFP as volume therapy. doi: http://dx.doi.org/10.4021/jh38e
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".