Bibliographic record
Abstract
The metabolic syndrome is described by the clustering of several risk factors for Type 2 diabetes and cardiovascular disease. Lipid disorder, obesity, diabetes in general and high blood pressure are collectively defined as risk factors for cardiovascular disease triggered by metabolic syndrome. The metabolic syndromes have a correlation with the variations in genetic susceptibility, nutritional regiment, physical exercise, chronological age and gender which play direct role in the incidence of metabolic syndrome and its side effects. There are several definitions of Metabolic Syndrome in the World: World Health Organization's (WHO), The NCEP Adult Treatment Panel (ATP) III and the International Diabetes Federation (IDF). It appears that the female type 2 diabetic patients need to change their life style to halt the burden of cardiovascular complications in type 2 diabetic patients . Clinicians should significantly consider screening all people regardless of age for abnormalities in glucose level. Early treatment in people with abnormal glucose level constitutes a strategy of preventing type 2 diabetes mellitus and metabolic syndrome. Studies about metabolic syndrome have shown that females were more affected than males. This may be due to the specific characteristics in the lifestyle changes between females and males diabetic patients. Postmenopausal status might be a predictor of metabolic syndrome. Some related factors of metabolic syndrome among postmenopausal women may increase cardiovascular risk in postmenopausal women. doi: http://dx.doi.org/10.4021/jem118e
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".