Development and validation of Transfusion Risk Understanding Scoring Tool (TRUST) to stratify cardiac surgery patients according to their blood transfusion needs
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Allogeneic blood transfusion is associated with transfusion reactions, infection transmission, and postoperative morbidity and mortality. The objective of this study was to develop and validate an accurate and simple clinical index to stratify cardiac surgery patients according to their blood transfusion needs. METHODS AND RESULTS: Data on consecutive adult patients who underwent cardiac surgery at Toronto General Hospital (n = 11,113) and Sunnybrook and Women's College Health Sciences Center (n = 5316) between May 1999 and June 2004 were collected for the development, validation, and external validation of the index. Primary outcome was the exposure to blood transfusion in the operative and first postoperative days. Multivariable logistic regression modeling techniques were used to determine the relationship between each independent variable and the exposure to allogeneic blood transfusion. Score assignment for each predictor variable was based on its regression coefficient. The predicted probabilities at each total score were compared to the observed proportions of patients exposed to blood transfusion. The clinical tool consists of eight preoperative variables: preoperative hemoglobin, weight, female sex, age, nonelective procedure, preoperative creatinine, previous cardiac surgical procedure, and nonisolated procedure. CONCLUSIONS: Based on the standards of measurement in clinical research, a valid clinical tool was developed for predicting the need for blood transfusion in patients undergoing cardiac surgery. The clinical tool was internally and externally validated, and the results suggest that it should perform well at other institutions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it