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 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".