Transfusion-associated circulatory overload and transfusion-related acute lung injury
Bibliographic record
Abstract
Transfusion-associated circulatory overload (TACO) and transfusion-related acute lung injury (TRALI) are syndromes of acute respiratory distress that occur within 6 hours of blood transfusion. TACO and TRALI are the leading causes of transfusion-related fatalities, and specific therapies are unavailable. Diagnostically, it remains very challenging to distinguish TACO and TRALI from underlying causes of lung injury and/or fluid overload as well as from each other. TACO is characterized by pulmonary hydrostatic (cardiogenic) edema, whereas TRALI presents as pulmonary permeability edema (noncardiogenic). The pathophysiology of both syndromes is complex and incompletely understood. A 2-hit model is generally assumed to underlie TACO and TRALI disease pathology, where the first hit represents the clinical condition of the patient and the second hit is conveyed by the transfusion product. In TACO, cardiac or renal impairment and positive fluid balance appear first hits, whereas suboptimal fluid management or other components in the transfused product may enable the second hit. Remarkably, other factors beyond volume play a role in TACO. In TRALI, the first hit can, for example, be represented by inflammation, whereas the second hit is assumed to be caused by antileukocyte antibodies or biological response modifiers (eg, lipids). In this review, we provide an up-to-date overview of TACO and TRALI regarding clinical definitions, diagnostic strategies, pathophysiological mechanisms, and potential therapies. More research is required to better understand TACO and TRALI pathophysiology, and more biomarker studies are warranted. Collectively, this may result in improved diagnostics and development of therapeutic approaches for these life-threatening 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".