Cardiac troponins and coronary artery calcium score: a systematic review
Bibliographic record
Abstract
An early diagnosis of atherosclerosis, particularly in subclinical status, can play a remarkable role in reducing mortality and morbidity. Because of coronary artery calcification (CAC) nature in radiation exposure, finding biomarkers associated with CAC could be useful in identifying individuals at high risk of CAC score. In this review, we focused on the association of cardiac troponins (hs-cTns) and CAC to achieve insight into the pathophysiology of CAC. In October 2022, we systematically searched Web of Science, Scopus, PubMed, and Embase databases to find human observational studies which have investigated the association of CAC with cardiac troponins. To appraise the included articles, we used the Newcastle Ottawa scale (NOS). Out of 520 records, 10 eligible studies were included. Based on findings from longitudinal studies and cross-sectional analyses, troponin T and I were correlated with occurrence of CAC and its severity. Two of the most important risk factors that affect the correlation between hs-cTns serum levels and CAC were age and gender. The elevation of cardiac troponins may affect the progression of CAC and future cardiovascular diseases. Verifying the association between cardiac troponins and CAC may lead to identify individuals exposed to enhanced risk of cardiovascular disease (CVD) complications and could establish innovative targets for pharmacological therapy.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".