Transfusion‐associated circulatory overload (<scp>TACO</scp>): Time to shed light on the pathophysiology
Bibliographic record
Abstract
Transfusion‐associated circulatory overload ( TACO ) is the most frequent pulmonary complication of transfusion and one of the leading causes of transfusion‐related fatalities. TACO is characterized by new or worsening hydrostatic pulmonary oedema which occurs within 6 h of blood transfusion and results in respiratory distress. Major risk factors for TACO include cardiac failure, renal dysfunction and the degree of positive fluid balance. Suboptimal fluid management and inappropriate infusion practices have also been reported as risk factors for TACO . Unfortunately, the TACO pathophysiology is poorly understood. It can be hypothesized that the pathophysiology of TACO may be generally reflected by a 2‐hit model. The first hit in TACO may be represented by the poor adaptability for volume overload in the transfusion recipient, which is supported by the identification of cardiovascular and renal risk factors for the onset of TACO . The second hit may subsequently be conveyed by the transfused blood product. Remarkably, volume overload alone does not appear to be the only factor triggering the onset of TACO. It can be hypothesized that other factors in the transfused product may play a significant role. In addition, inflammation in the transfused recipient may also be an important feature in TACO . To obtain more insight into the TACO pathophysiology, it will be important to assess and validate potential biomarkers in TACO . In addition, development of currently unavailable TACO ‐animal models will provide a useful tool for dissecting the pathophysiologic mechanisms. Collectively, this will aid in improving diagnostic approaches and shed light on possible therapeutic interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".