Utilization of machine learning to model the effect of blood product transfusion on short‐term lung transplant outcomes
Bibliographic record
Abstract
The objective of this study was to identify the relationship between blood product transfusion and short-term morbidity and mortality following lung transplantation utilizing machine learning. Preoperative recipient characterstics, procedural variables, perioperative blood product transfusions, and donor charactersitics were included in the model. The primary composite outcome was occurrence on any of the following six endpoints: mortality during index hospitalization; primary graft dysfunction at 72 h post-transplant or the need for postoperative circulatory support; neurological complications (seizure, stroke, or major encephalopathy); perioperative acute coronary syndrome or cardiac arrest; and renal dysfunction requiring renal replacement therapy. The cohort included 369 patients, with the composite outcome occurring in 125 cases (33.9%). Elastic net regression analysis identified 11 significant predictors of composite morbidity: higher packed red blood cell, platelet, cryoprecipitate and plasma volume from the critical period, preoperative functional dependence, any preoperative blood transfusion, VV ECMO bridge to transplant, and antifibrinolytic therapy were associated with higher risk of morbidity. Preoperative steroids, taller height, and primary chest closure were protective against composite morbidity.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".