The relationship between alcohol consumption and erectile dysfunction in men: a systematic review
Bibliographic record
Abstract
Introduction: Erectile dysfunction is one of the most common sexual health problems in men and is defined by the persistent inability to achieve or maintain an erection for sexual performance. Several risk factors have been mentioned regarding its occurrence. Alcohol is a central nervous system depressant that can lead to erectile dysfunction. The present study was conducted with the aim of a systematic review of the studies conducted on the relationship between erectile dysfunction and alcohol consumption. Methods: The present study was conducted by searching in reliable databases of Web of Science, PubMed, Scopus and Google Scholar search engine without time limit until 2024 by two independent researchers and using Mesh keywords including: Dysfunction, Erectile, Alcohol Drinking, Alcoholism, and Male Sexual Impotence. To evaluate the quality of the articles, the Newcastle-Ottawa scale was used for observational studies or the Cochrane risk of bias tool for interventional studies. Results: After reviewing and evaluating the quality of 196 selected articles, finally 15 articles were included in the systematic review. The results of most studies show that alcohol consumption is effective in the occurrence of erectile dysfunction in men. The duration, frequency and amount of alcohol consumption were also effective on the severity and occurrence of erectile dysfunction. It has also been shown that avoiding alcohol can be effective in improving penile erection. Conclusion: Despite the contradictory results of some studies, the findings of most studies show the existence of a relationship between alcohol consumption and erectile dysfunction.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".