Clinical Predictions and Decisions to Perform Cardiac Surgery on High-Risk Patients
Bibliographic record
Abstract
The proportion of high-risk patients undergoing cardiac surgery has increased steadily over the last two decades. Many of those patients have a catastrophic postoperative course and use hospital resources in a proportion that largely outweighs their number. Consequently, the appropriateness of invasive and intensive interventions in those patients has been questioned. If futility of care were predictable preoperatively, cardiac surgery would probably be denied to many high-risk patients. Logistic regression has been used to develop many complex predictive models to identify high-risk patients and predict their outcome; however, those models do not provide much more discrimination than clinical judgment alone. Moreover, with continuous improvement in medical care all risk models lose their calibration over time. As a result, they often overestimate the probabilities of poor outcome in the individual patients. Many high-risk cardiac surgical patients require a prolonged stay in the intensive care unit (ICU). The analysis of small cohorts of patients who had a prolonged postoperative stay in the ICU shows that 50% and 40% of them are still alive at 1- and 2-year follow-up, respectively; and most survivors report a good quality of life. Considering the limitations of predictive risk models and the satisfaction of cardiac surgical patients who survive after a prolonged ICU stay, it is reasonable to recognize that cardiac surgery should rarely be denied to high-risk patients unless technically unfeasible, and clinical predictions should have only a marginal role in the decision to operate on those patients.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".