Metabolic syndrome and its association with morbidity and mortality
Bibliographic record
Abstract
The metabolic syndrome (MetS) is a cluster of cardiovascular risk factors that are associated with increased risk of diabetes, cardiovascular disease (CVD), and all-cause mortality; however, it is clear that considerable variation exists in these relationships. Given that the prevalence of MetS increases with age, is higher in men than in women, and varies with race and ethnicity, a number of questions about the clinical application of MetS in predicting morbidity and mortality in diverse populations remain unanswered. Thus, in this review, we compare the ability of MetS to predict health risk across age, sex, race, and ethnicity, and in primary versus secondary prevention subgroups to explore these relationships. Furthermore, as there is currently no universal MetS criteria, we also discuss differences in the prediction of morbidity and mortality in studies that used different criteria to define MetS. At present, further research is necessary to examine the health risks associated with (i) different combinations of MetS components in diverse populations, (ii) the relative importance of each MetS component in predicting different health outcomes, and (iii) the independent contribution of MetS in predicting risk of morbidity and mortality beyond that incurred by other risk factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".