Obesity, Dyslipidemias and Erectile Dysfunction: A Report of a Subcommittee of the Sexual Medicine Society of North America
Bibliographic record
Abstract
Because of increasingly sedentary lifestyles and diets higher in saturated fats, obesity and dyslipidemias are common and increasing in prevalence in Westernized countries. Longitudinal population-based studies clearly demonstrate that dyslipidemias and obesity, as well as factors such as hypertension, diabetes, and smoking, are major risk factors for atherosclerosis. Both clinical and animal models of endothelial dysfunction confirm that atherosclerosis leads to increased cerebrovascular and cardiovascular morbidity. Clinical studies with hypolipidemic agents demonstrate that hydroxy-3-methylglutaryl coenzyme A reductase inhibitors can decrease the risk of vascular morbidity. An increasing body of evidence from animal models demonstrates that hypercholesterolemia and atherosclerosis are risk factors for the development of erectile dysfunction (ED). This causal relationship between obesity and dyslipidemias with the development of ED in humans still needs further definition with convincing peer-reviewed scientific studies. The challenge for the future will be to define the benefit of controlling obesity and dyslipidemias on the development of ED and improvement of erectile function.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".