<scp>NT‐proBNP</scp> levels in the identification and classification of pulmonary transfusion reactions
Bibliographic record
Abstract
BACKGROUND: Consensus definitions for transfusion-related acute lung injury (TRALI) and transfusion-associated circulatory overload (TACO) have recently been revised; however, pulmonary transfusion reactions remain difficult to diagnose. We hypothesized that N-terminal pro-brain natriuretic peptide (NT-proBNP) levels could have utility in the identification and classification of pulmonary transfusion reactions. STUDY DESIGN AND METHODS: We performed a secondary analysis of a case-control study of pulmonary transfusion reactions at four academic hospitals. We evaluated clinical data and measured NT-proBNP levels prior to and following transfusion in patients with TACO (n = 160), transfused acute respiratory distress syndrome (ARDS) [n = 51], TRALI [n = 12], TACO/TRALI [n = 7], and controls [n = 335]. We used Wilcoxon Rank-Sum tests to compare NT-proBNP levels, and classification and regression tree (CART) algorithms to produce a ranking of covariates in order of relative importance for differentiating TACO from transfused controls. RESULTS: Pre-transfusion NT-proBNP levels were elevated in cases of transfused ARDS and TACO (both P < .001) but not TRALI (P = .31) or TACO/TRALI (P = .23) compared to transfused controls. Pre-transfusion NT-proBNP levels were higher in cases of transfused ARDS or TRALI with a diagnosis of sepsis compared to those without (P < .05 for both). CART analyses resulted in similar differentiation of patients with TACO from transfused controls for models utilizing either NT-proBNP levels (AUC 0.83) or echocardiogram results (AUC 0.80). CONCLUSIONS: NT-proBNP levels may have utility in the classification of pulmonary transfusion reactions. Prospective studies are needed to test the predictive utility of pre-transfusion NT-proBNP in conjunction with other clinical factors in identifying patients at risk of pulmonary transfusion reactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".