Development of a predictive model for major adverse cardiac events in a coronary artery bypass and valve population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Quality improvement initiatives in cardiac surgery largely rely on risk prediction models. Most often, these models include isolated populations and describe isolated end-points. However, with the changing clinical profile of the cardiac surgical patients, mixed populations models are required to accurately represent the majority of the surgical population. Also, composite model end-points of morbidity and mortality, better reflect outcomes experienced by patients. METHODS: The model development cohort included 4,270 patients who underwent aortic or mitral valve replacement, or mitral valve repair with/without coronary artery bypass grafting, or isolated coronary artery bypass grafting. A composite end-point of infection, stroke, acute renal failure, or death was evaluated. Age, sex, surgical priority, and procedure were forced, a priori, into the model and then stepwise selection of candidate variables was utilized. Model performance was evaluated by concordance statistic, Hosmer-Lemeshow Goodness of Fit, and calibration plots. Bootstrap technique was employed to validate the model. RESULTS: The model included 16 variables. Several variables were significant such as, emergent surgical priority (OR 4.3; 95% CI 2.9-7.4), CABG + Valve procedure (OR 2.3; 95% CI 1.8-3.0), and frailty (OR 1.7; 95% CI 1.2-2.5), among others. The concordance statistic for the major adverse cardiac events model in a mixed population was 0.764 (95% CL; 0.75-0.79) and had excellent calibration. CONCLUSIONS: Development of predictive models with composite end-points and mixed procedure population can yield robust statistical and clinical validity. As they more accurately reflect current cardiac surgical profile, models such as this, are an essential tool in quality improvement efforts.
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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 | 0.002 |
| 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 it