Bibliographic record
Abstract
PURPOSE OF REVIEW: We presume that biomarkers will improve identification of patients at risk, leading to interventions and treatments that reduce perioperative adverse events. Risk stratification is multifactorial, and a biomarker must add information to this process, thereby redistributing patients to either higher or lower risk categories, to improve the allocation of expensive and risky interventions. This review focuses on the utility of three cardiac biomarkers in perioperative management. RECENT FINDINGS: Using newly defined epidemiologic criteria, three distinct molecules, brain natriuretic peptide (BNP), troponin (cTn), and glycosylated hemoglobin (HbA1c) emerge as potentially useful in perioperative medicine. A meta-analysis shows, in vascular surgery, BNP improves risk stratification. Four articles highlight the utility of postoperative cTn measurements in cases of myocardial injury. These articles show that most injury is not infarction, and they present preliminary evidence of the populations that will benefit from structured surveillance protocols. HbA1c is shown to improve the prediction of mortality, but there are questions whether this risk is modifiable. SUMMARY: The findings here suggest an expanded role for postoperative cTn surveillance; however, the precise populations that benefit, or the interventions required, are not yet defined. The encouraging data for the other two biomarkers need more investigations before adopting them into routine clinical use.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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 | 0.000 | 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".