Erectile Dysfunction: A Harbinger of Cardiovascular Disease Risk
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are a significant source of morbidity and mortality despite improvements in treatment. The recognition of CV risk and the early identification of CVD are crucial to facilitate early intervention and preventative strategies. This chapter presents a critical review of the literature on the role of erectile dysfunction (ED) as an early marker for CVD risk. ED and CVD share a number of risk factors, while ED itself may be considered an important risk factor for CVD, particularly in younger men with arteriogenic ED. Epidemiological data support an association between ED and CVD, with reports suggesting that ED often occurs years prior to the onset of CVD. This reflects the shared pathophysiological mechanisms of ED and CVD and the earlier emergence of symptoms in small-diameter arteries, such as those in penile tissue, secondary to atherosclerosis and endothelial dysfunction. The importance of these observations is considered with respect to the clinical implications of studies exploring the predictive value of ED for future CVD risk. The chapter concludes with clinical insights into the role of ED in CVD risk assessment and the integration of ED assessment in routine CVD health screenings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".