Research Progress of Risk Prediction Models for Patients Undergoing Cardiac Surgery
Bibliographic record
Abstract
Surgical risk prediction is to predict postoperative morbidity and mortality with internationally authoritative mathematical models. For patients undergoing high-risk cardiac surgery,surgical risk prediction is helpful for decisionmaking on treatment strategies and minimization of postoperative complications,which has gradually arouse interest of cardiac surgeons. There are many risk prediction models for cardiac surgery in the world,including European System for Cardiac Operative Risk Evaluation(EuroSCORE),Ontario Province Risk(OPR) score,Society of Thoracic Surgeons(STS)score,Cleveland Clinic risk score,Quality Measurement and Management Initiative(QMMI),American College of Cardiology/American Heart Association(ACC/AHA) Guidelines for Coronary Artery Bypass Graft Surgery,and Sino System for Coronary Operative Risk Evaluation(SinoSCORE). All these models are established from the database of thousands or ten thousands patients undergoing cardiac surgery in a specifi c region. As different sources of data and calculation imparities exist,there are probably bias and heterogeneities when the models are applied in other regions. How to decrease deviation and improve predicting effects had become the main research target in the future. This review focuses on the progress of risk prediction models for patients undergoing cardiac surgery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".