A multivariable model for predicting the need for blood transfusion in patients undergoing first‐time elective coronary bypass graft surgery
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The incidence of blood transfusion in coronary artery bypass graft (CABG) surgery remains high. Preoperative identification of those at high risk for requiring blood will allow for the cost-effective use of some blood conservation modalities. Multivariable analysis techniques were used in this study to develop a prediction rule for such a purpose. STUDY DESIGN AND METHODS: Data were prospectively collected for all patients undergoing elective first-time CABG surgery from January 1997 to September 1998 at a tertiary-care teaching hospital (n = 1007). The prediction rule was developed on the first two-thirds of the sample by using logistic regression methods to examine the relationship of patient demographics, comorbidities, and preoperative Hb with perioperative blood transfusion. The remaining one-third of the sample was used to validate the rule. RESULTS: The transfusion rate was 29.4 percent. The prediction rule included preoperative Hb (g/dL, OR 0.928, p<0.0001), weight (kg, OR 0.938, p<0.0001), age (years, OR 1.037, p<0.01), and sex (male/female, OR 0.493, p<0.01); receiver operating characteristic = 0.86. When externally validated, the rule had a sensitivity of 82.1 percent and a specificity of 63.6 percent (at a selected probability cutoff). CONCLUSION: A simple and valid prediction rule is developed for predicting the risk of blood transfusion in patients undergoing first-time elective CABG surgery.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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