Transfusion Predictors in Liver Transplant
Bibliographic record
Abstract
UNLABELLED: In this study we sought to determine the factors influencing red blood cell (RBC) transfusions and to study the transfusion practice of anesthesiologists during liver transplants. A retrospective study of 206 successive liver transplants was undertaken during a period of 52 mo. Transfused blood products were identified. Twenty variables were analyzed in a univariate fashion. For the multivariate analysis, the cases were divided in 2 subgroups: more than 4 RBC units transfused and 4 or less RBC units transfused. The average number of RBC units transfused during a liver transplant was 2.8 (+/- 3.5) per patient, 32.0% did not receive any RBC, and 19.4% did not receive any blood products during the transplant. Three variables were related to the number of RBC units transfused: the starting International Normalized Ratio value, the starting platelet count, and the duration of surgery. We found that there was a wide difference in the transfusion practice of the anesthesiologists involved in this series of liver transplants. It was difficult to identify predictive factors for RBC transfusions when the transfusion rate was small and because of the variability in human factors. Plasma transfusion did not decrease the rate of RBC transfusions; sometimes it was the contrary. IMPLICATIONS: This is a retrospective study of 206 liver transplants over 52 mo to identify the predictive factors of red blood cell transfusions and the anesthesiologists' transfusion strategies. We conclude that there is a wide difference in transfusion practices among anesthesiologists.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".