Bibliographic record
Abstract
PURPOSE OF REVIEW: Obesity has reached global epidemic proportions because of an increasingly obesogenic environment. This review examines the association between obesity, and in particular visceral fat, as a risk factor for cardiovascular disease and mortality. RECENT FINDINGS: The World Health Organization defines obesity based on the body mass index. Recently the waist-to-hip ratio has been shown to be a significantly stronger predictor of cardiovascular events than body mass index. The metabolic syndrome and its evolving definition represent a cluster of metabolic risk factors which help predict cardiovascular disease and mortality. Although insulin resistance plays a central role in the pathophysiology of the metabolic syndrome, there is limited support for therapy with insulin sensitizers, thiazolidinediones, in patients with coronary artery disease. The current anti-obesity drugs, orlistat and sibutramine, have only a modest effect on weight loss. The blockade of the endocannabinoid system with rimonabant, however, may be a promising new strategy. SUMMARY: Obesity is associated with significant increase in cardiovascular risk. Lifestyle modification remains the cornerstone of management although anti-obesity medications may be indicated in high risk individuals with comorbid disease.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".