Development of tools to assess visceral adipose tissue (VAT) accumulation during the development of erectile dysfunction (ED) and during pharmacotherapy
Bibliographic record
Abstract
ED is a multi‐factorial process involving cardiovascular, metabolic and hormonal factors. In particular, obesity is in epidemic proportions, with the incidence of ED in obese men reaching ~80%. Despite this, few animal models have mechanistically linked ED and obesity, and none have yet provided a testing ground for new pharmacotherapeutic strategies in obesity‐related ED. The present study was performed to begin to address this gap. Erectile responses were induced using the dopaminergic agonist, apomorphine (80 ug/kg, s.c.), in aging (15–75 wks) Sprague‐Dawley and Wistar rats. Assessment of physical properties included: body weight (BW), body length (BL), waist circumference (WC), and post‐mortem VAT mass. MR imaging was also used to assess adiposity vs. post‐mortem data. Erections declined in an age‐related (20–75 wks) manner, paralleling increases in BW and 7‐fold increases in VAT. Regression analysis revealed the best non‐invasive indicators of VAT by rank order of R 2 were: BW:BL ratio > WC >> BW. These studies demonstrate the importance of assessing age‐related changes in physical features and adiposity (particularly VAT), with respect to changes in penile vascular responses. Previous studies have linked recovery of erectile responses with weight loss; whether this improvement in function is specifically linked to reductions in VAT is currently being studied. (Funds: Heart and Stroke Foundation)
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".