Cystatin C as Predictor of Long-Term Mortality in Elderly: a Systematic Review and Meta-Analysis
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Bibliographic record
Abstract
Abstract Prediction of mortality in growing aged population will offer several benefits for health sector. Cystatin C, which has long been known as biomarker to more accurately evaluate glomerular filtration rate in elderly, has also been shown to predict mortality from several studies. Studies regarding its predictive ability were vastly varied, and there has not been systematic review to examine its ability in predicting long-term mortality in elderly population. This study aimed to evaluate cystatin C performance as predictor for all-cause and cardiovascular mortality among elderly population. A systematic review of prospective cohort studies was performed. Literature searching was done in major databases such as PubMed, Cochrane, Scopus, EBSCOhost, and ProQuest. Manual searching was also performed. Inclusion criteria were studies involving elderly age 65 or older, cystatin C serum levels available, all-cause mortality as outcome, and 5-year minimum of follow-up. Study selection was performed according to PRISMA algorithm. Newcastle–Ottawa scale for cohort study was used to assess primary studies’ quality and risk of bias. Study results were presented in descriptive tables and forest plot. Initial searching revealed 609 hits with 12 studies eligible for the review: five studies evaluated all-cause mortality, three studies evaluated cardiovascular mortality, and four studies evaluated both. Meta-analysis showed that high cystatin C levels are increasing risk of long-term all-cause mortality [( HR : 1.74 (95% CI : 1.48–2.04); p < 0.0001)] and cardiovascular mortality [ HR : 2.01 (95% CI : 1.63–2.47); p < 0.0001)]. The prognostic ability of cystatin C was considerably moderate [ AUC 0.70 (95% CI : 0.68–0.72); p = 0.02]. Cystatin C was able to predict long-term mortality in elderly population.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.030 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it