Are Coronary Angiograms of Value in the Risk Stratification of Patients Undergoing Coronary Artery Bypass Surgery?
Bibliographic record
Abstract
INTRODUCTION: There are currently more than 20 risk-scoring systems that attempt to predict peri-operative mortality following coronary artery bypass surgery (CABG). All these scoring systems use objective criteria to assess operative risk. Angiographic data are currently not included in any of these systems. This pilot study assessed the value of coronary angiography in predicting peri-operative mortality following CABG. PATIENTS AND METHODS: Fourteen patients who died following first-time isolated CABG surgery were identified. These were matched with 14 patients of similar age, sex, left ventricle function and European System for Cardiac Operative Risk Evaluation (EuroSCORE). A panel of 25 clinicians were given details of the patients' age, sex, diabetic status, family history, smoking history, hypertensive status, lipid status, pre-operative symptoms, left ventricle ejection fraction and weight and shown the coronary angiograms of the patient. They were asked to predict the outcome following CABG for each patient. RESULTS: Receiver operator characteristic curves were constructed and the area under the curves calculated and analysed using a commercially available statistical package (PRISM). The area under the curve for the group was 0.6820 for the group. Consultant clinicians achieved an area of 0.6789 versus their trainees 0.6844 (P = NS). The cardiologists achieved an area of 0.7063 versus the cardiothoracic surgeons 0.6491 (P = NS). CONCLUSIONS: Despite the EuroSCORE predicting equal risk for the two groups of patients, it would appear that clinicians are able to identify individual higher risk patients by assessing pre-operatively the quality of the patient's coronary vasculature. Although the clinicians were able to predict individual patient mortality better than the EuroSCORE, the area under the curve indicates that it is not a robust method and clinicians, with all the clinical information to hand, are only moderately good at predicting the outcome following coronary artery bypass 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".