Association of sST2 with cardiovascular disease in maintenance hemodialysis patients: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Previous studies have shown that soluble growth-stimulated expression gene 2 protein (sST2) plays a significant role in the progression of maintenance hemodialysis. However, the relationship between sST2 and cardiovascular events in maintenance hemodialysis patients remains largely understudied. Method This study systematically searched PubMed, Wanfang, and other Chinese and English databases for all published studies on sST2 in maintenance hemodialysis. Relevant literature meeting the requirements was screened, and relevant data were extracted accordingly. The Newcastle–Ottawa Scale was used to evaluate the quality of the selected studies. Publication bias was assessed via funnel plots and Egger’s test. A meta-analysis was performed using either a random-effects or fixed-effects model, as appropriate. Results A total of 10 high-quality studies were included in this study, with no significant publication bias. The meta-analysis revealed that sST2 level was associated with the incidence of cardiovascular events in maintenance hemodialysis patients (mean difference = 18.89, 95% confidence interval: 13.93–23.84; p < 0.001). Moreover, elevated sST2 levels were correlated with a higher risk of cardiovascular events (odds ratio = 2.89, 95% confidence interval: 1.71–4.89; p < 0.0001). Conclusion sST2 may serve as a predictive biomarker for cardiovascular events in maintenance hemodialysis patients.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".