Predictive value of plasma brain natriuretic peptide for cardiac outcome after vascular surgery
Bibliographic record
Abstract
Vascular surgery is associated with a substantial risk of cardiovascular events and death.1,2 There is no effective method for determining cardiac risk preoperatively: validated risk prediction instruments are limited by complexity and poor predictive value, and other cardiac investigations such as nuclear stress testing and coronary angiography are limited by time and resources. For these reasons, alternative methods that can predict outcome of at risk patients would be an important advance. Plasma brain natriuretic peptide (BNP) has counter-regulatory vasodilator and natriuretic properties. Plasma BNP concentrations are often increased in cardiac disorders, such as angina and heart failure. The plasma concentrations of BNP are related to prognosis in these conditions.3 Many of these cardiovascular conditions occur in patients with peripheral vascular disease. We investigated the predictive value of preoperative plasma BNP concentration for the occurrence of perioperative fatal or non-fatal myocardial infarction (MI) in high risk vascular surgical patients. We also compared the predictive value of plasma BNP concentration with the Eagle score, a conventional surgical risk assessment instrument.1,2 We screened consecutive patients undergoing major surgery for aortic or peripheral arterial occlusive disease in Gartnavel General Hospital, Glasgow, between April and September 2004. All patients at high risk, defined according to the American Society of Anesthesiology …
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".