The Metabolic Syndrome: Definitions, Prevalence and Management
Bibliographic record
Abstract
The metabolic syndrome (MetS) refers to the clustering of various metabolic risk factors that include abdominal obesity, dyslipidemia, hypertension, and hyperglycemia. It is now well known that it is associated with an increased risk of cardiovascular disease (CVD) and of type 2 diabetes. The increasing prevalence of the MetS, associated with the substantial progression of obesity and diabetes, is therefore an important public health concern. Over the years, several definitions for the MetS have been proposed by major scientific associations. Those definitions differ somewhat in their criteria and threshold values but generally all agree on the essential components of the syndrome. The proposed definitions are intended to help identify individuals at increased long-term risk of CVD, who could benefit from early prevention. The diagnosis of the MetS should be used concurrently with standard predicting algorithms, such as the Framingham Risk Score and the Diabetes Predicting Model, which better predict short-term risks. The management of the MetS should emphasize therapeutic lifestyle modifications--weight loss, increased physical activity, healthy diet--as the first-line therapy. If the short-term risk of CVD or diabetes is high, specific risk factors should be monitored more closely according to established guidelines and drug therapy may be appropriate.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".