Diagnostic Value of sST2 in Cardiovascular Diseases: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Biomarkers such as B-type natriuretic peptide (BNP), N-terminal pro-BNP (NT-proBNP), cardiac troponin (cTn), and CK-MB contribute significantly to the diagnosis of cardiovascular disease (CVD). Recent studies have demonstrated that suppression of tumorigenicity 2 (ST2) is associated with CVD, but a meta-analysis of ST2 levels in different CVDs has yet to be conducted. Therefore, the present study aimed to investigate soluble ST2 (sST2) levels in patients with ischemic heart disease (IHD), myocardial infarction (MI), and heart failure (HF). A total of 1,425 studies were searched across four databases, of which 16 studies were included in the meta-analysis. The Newcastle–Ottawa Quality Assessment Scale (NOS) values of all 16 studies were ≥7. The meta-analysis results indicated that the sST2 level was not correlated with IHD (standard mean difference [SMD] = 0.58, 95% confidence interval [95% CI] = 0.00 to 1.16, p = 0.05) or MI (weighted mean difference [WMD] = 0.17, 95% CI = −0.22 to 0.55, p = 0.40) but was significantly associated with HF (WMD = 0.21, 95% CI = 0.04 to 0.38, p = 0.02; I 2 = 99%, p < 0.00001). sST2 levels did not differ significantly between patients with IHD or MI and healthy individuals; however, we believe that ST2 could be used as an auxiliary diagnostic biomarker of HF.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".