The correlation between high-sensitivity troponin-T and cell-free cardiac DNA in the blood of patients undergoing noncardiac, predominantly vascular surgery
Bibliographic record
Abstract
OBJECTIVE: To present a novel method that uses an epigenetic fingerprint to measure changes in plasma concentrations of cardiac-specific cell-free DNA (CS-cfDNA) as a marker of myocardial cell death. METHODS: This prospective, analytic, observational comparative study included patients with heart disease or multiple risk factors for heart disease undergoing major noncardiac, mostly vascular surgery, requiring an arterial-line, and at least 24 h hospitalization in the post anaesthesia care unit or critical care unit after surgery. Blood samples were collected at least four times per patient to measure troponin-T (via high-sensitivity troponin-T test) and CS-cfDNA pre- and postoperatively. RESULTS: A total of 117 patients were included (group 1, 77 patients [66%] with low preoperative and postoperative troponin-T; group 2, 18 patients [15%] with low preoperative but increased postoperative troponin-T; group 3, 16 patients [14%] with high troponin-T both preoperatively and postoperatively; and group 4, six patients [5%] with elevated preoperative troponin-T that decreased postoperatively). The increase in CS-cfDNA after surgery was statistically significant only in group 2, which correlated with an increase in troponin-T in the same group. CONCLUSIONS: CS-cfDNA increased early postoperatively, particularly in patients with silent postoperative troponin elevation, and was correlated with an increase in troponin-T. These results may suggest that, in the subgroup of patients with postoperative elevated troponin, cardiomyocyte death indeed occurred.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".