Prevalence of mental health evaluation in erectile dysfunction clinical trials
Bibliographic record
Abstract
BACKGROUND: Erectile dysfunction (ED) is commonly psychogenic or may cause psychological issues, such as anxiety and depression. Nevertheless, the inclusion of mental health (MH) assessment in ED clinical trials has yet to be quantified. AIM: To evaluate the prevalence of MH evaluation in ED clinical trials. METHODS: The brief and detailed descriptions from every clinical trial concerning ED from the US National Library of Medicine ClinicalTrials.gov database were extracted. OUTCOMES: The number of studies which mention the terms "self-esteem", "anxiety", "depression", "schizophrenia", "emasculation", "humiliation", "isolation", "loneliness", "frustration", "OCD", "PTSD", "ADHD", "SUD", "BPD", "autism", "bipolar", "dementia", "phobia", "mania", "anorexia", "bulimia", "insomnia", and "delirium" were assessed. RESULTS: In total, 453 clinical trials were included from 1988 to 2024. Only seven of the searched MH terms were present in any clinical trial: stress (n = 3), self-esteem (n = 11), anxiety (n = 15), depression (n = 17), bipolar (n = 1), insomnia (n = 1), and isolation (n = 1). There was no temporal improvement in the prevalence of MH terms over time. CLINICAL IMPLICATIONS: The limited inclusion of MH terms underscores a potential gap in addressing the psychological dimensions of ED in a clinical setting. Such considerations may enhance patient care by improving diagnosis and MH outcomes. STRENGTHS AND LIMITATIONS: This study employs a replicable methodology using automated data extraction to quantify MH representation in ED trials. However, limitations include strict word-matching and an inability to extract word-context. CONCLUSION: MH terms are infrequently included in ED clinical trials, which may reflect a lack of research interest in the association between ED and MH.
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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 | 0.304 | 0.552 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".