The Use of Biomarkers to Provide Diagnostic and Prognostic Information Following Cardiac Surgery
Bibliographic record
Abstract
Coronary artery bypass grafting (CABG) is utilized as a treatment for patients with multivessel coronary artery disease. More than 500,000 patients in the United States, Canada, and Europe undergo coronary artery bypass grafting each year, with an annual cost has been estimated to be at least 15 billion dollars per year (1) . While it has been estimated that cardiac morbidity and mortality may occur in up to 20% of patients who undergo this procedure, the accurate detection of those patients who suffer perioperative cardiac injury remains difficult, owing to the lack of a readily available gold standard for the detection of cardiac injury. It would be expected that detection of significant cardiac injury would be clinically and prognostically important, as the presence of myocardial cellular necrosis identifies patients at increased cardiovascular risk in situations as diverse as acute coronary syndromes (ACS), catheter-based interventions, pulmonary embolism, and severe medical illness. Thus, accurate detection of myocardial cellular necrosis in the setting of cardiac surgery should allow for the identification of patients who are at increased short-term risk. In addition, the ability to identify accurately the presence and degree of myocardial cellular necrosis after cardiopulmonary bypass would allow for improvement of the techniques utilized for myocardial protection utilized during cardiac surgery and cardiopulmonary bypass. Many of the techniques utilized during cardiac surgery are designed to minimize cellular trauma during the surgery itself. Without a sensitive and reliable gold standard, it is very difficult to compare alternative therapeutic techniques to determine the preferred method. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".