Prevalence of Metabolic Syndrome and Its Components in Workers of a High-Level Educational Institution, Cartagena-Colombia
Bibliographic record
Abstract
To determine the prevalence of metabolic syndrome, components and their relationship in workers of a higher education institution of Cartagena-Colombia. An analytical cross-sectional study was carried out, with a probabilistic sample of 162 workers in a higher education institution. Sociodemographic data were recorded, physical examination including abdominal circumference, blood pressure, height, weight, as well as clinical tests of lipid laboratory (total cholesterol, HDL cholesterol and triglycerides) and fasting glycemia determined by colorimetric and enzymatic methods. The parameters established by the American Heart Association-AHA were used to identify metabolic syndrome. A prevalence of metabolic syndrome of 11.1% was estimated. The most frequent components among individuals with metabolic syndrome were: increase in abdominal circumference (83.3%), hypertriglyceridemia (66.7%) and low levels of HDL-c (50.0%). The elevation of the abdominal perimeter was the most frequent component. No statistical evidence of association between any of the components of the syndrome was found. These evidences suggest to improve the life habits of the workers evaluated, in terms of their nutrition and physical activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".