Optimal Cut-off Points of the Standardized Continuous Metabolic Syndrome Severity Score (cMetS-S) for Predicting Cardiovascular Disease (CVD) and CVD Mortality in the Tehran Lipid and Glucose Study (TLGS)
Bibliographic record
Abstract
Background: Metabolic Syndrome (MetS) is a prevalent condition associated with an increased risk of cardiovascular disease (CVD) and CVD mortality. Due to the limited clinical applicability of MetS, the standardized continuous metabolic syndrome severity score (cMetS-S) has the potential to provide continuous assessment of metabolic risk. Objectives: This study evaluated the optimal cMetS-S cut-off points in the Tehran Lipid and Glucose Study (TLGS) for predicting CVD and CVD mortality. Methods: The study included 7,776 participants over 30 years old at baseline, followed for 18 years. Sex-specific sensitivity (SS) and specificity (SP) of cMetS-S measures for predicting CVD and CVD mortality were evaluated using a receiver operating characteristic (ROC) curve, along with the area under the curve (AUC), employing a naive estimator and considering event failure status and MetS variables. Results: The cut-off point of cMetS-S for CVD was 0.13 (SS: 65.5%, SP: 59.6%) for the total population, 0.44 (SS: 49.6%, SP: 68.1%) for men, and 0.27 (SS: 64.2%, SP: 69.2%) for women. The cut-off point of cMetS-S for CVD mortality was 0.53 (SS: 51.3%, SP: 71.9%) for the total population, 0.76 (SS: 35.1%, SP: 76.2%) for men, and 0.28 (SS: 78.8%, SP: 66.4%) for women. The AUC (95% CI) of MetS based on the International Diabetes Federation (IDF) and Joint Interim Statement (JIS) definitions were 60.0 (65.3 - 56.8) and 61.1 (59.6 - 56.8) for CVD, and 59.3 (56.0 - 62.5) and 59.4 (56.3 - 62.6) for CVD mortality. Conclusions: The cut-off points of cMetS-S for CVD and CVD mortality differ between men and women. The cMetS-S could be a better predictive tool for CVD and CVD mortality than MetS.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".