Obesity and Cardiovascular Disease: Pathogenic Mechanisms and Potential Benefits of Weight Reduction
Bibliographic record
Abstract
The prevalence of obesity in industrialized countries has reached epidemic proportions, with about one in three people being obese and another one in three people being overweight and at risk of developing obesity. In recent years, obesity has gained the traditional tetrad of cardiovascular risk factors of smoking: hypertension, dyslipidemia, and dysglycemia. Attention has also focused on the importance of abdominal (or central) obesity as a determinant of cardiovascular risk, independent of the body mass index. In addition to effects on coronary artery disease, obesity has an effect on cardiovascular disease, including stroke, ventricular function, peripheral arterial disease, and venous thromboembolism. The objectives of this review are to summarize the effects of obesity on cardiovascular disease, and the possible mechanisms for these associations, and to investigate the effects of weight-loss interventions on the burden of cardiovascular disease. Large ongoing clinical outcome trials, such as the SOS study, the Look-AHEAD trial, or the SCOUT study, should provide important information on the effects of surgical and nonsurgical obesity treatment on cardiovascular morbidity and mortality.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".