Bibliographic record
Abstract
INTRODUCTION: Cannabis (marijuana) is the most widely used illicit drug globally. Given the prevalence of nonprescription illicit drug abuse, there is a growing interest in the study of its potential effects on male sexual health. In this review, we discuss the effects of cannabis on male sexual health. OBJECTIVE: In this review, we discuss the effects of cannabis on male sexual health. METHODS AND MAIN OUTCOME MEASURE: Critical review of scientific literature examining the impact of cannabis use on male sexual health. RESULTS: Studies examining the effects of cannabis use on male sexual function have been limited in both quality and quantity. Most results of these studies are conflicting and contradictory. While some did outline the beneficial effects of cannabis in enhancing erectile function, others did not. However, recent animal and in vitro studies have identified potential links between cannabis and sexual health. It appears that cannabis may actually have peripheral antagonizing effects on erectile function by stimulating specific receptors in the cavernous tissue. CONCLUSIONS: Given the prevalence of cannabis use, and the potential relationships between use and the development of potentially hazardous effects on male sexual function, we encourage renewed use of research resources to determine in-depth mechanistic knowledge, and new clinically oriented studies examining the effect of cannabis on male sexual function.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".