Prevalence of erectile dysfunction as long-COVID symptom in hospitalized Japanese patients
Bibliographic record
Abstract
Coronavirus disease-2019 (COVID-19) is associated with a wide range of post-acute sequelae. The prevalence of erectile dysfunction (ED) that developed after COVID-19 and the associated underlying factors were analyzed based on a questionnaire survey, COVID-19 Recovery Study II in Japan. A case-control study was conducted with those with or without ED one and two years hospitalized with COVID-19 between March and September 2021. Six hundred and nine Japanese men, with a median age of 48 years, were analyzed. During the study period, 116 subjects (19.0%) had erectile dysfunction. The patients with ED responded with less subjective awareness of recovery and high breathless and fatigue scores compared to those without ED. The patients with ED also showed higher Hospital Anxiety and Depression Scale-D (depression) and the EuroQol 5-dimensions 5-level scores for pain/discomfort and anxiety/depression scores compared before COVID-19 infection. Sleep disturbance was suggested to be associated with erectile dysfunction using an exploratory clustering analysis in the one-year survey. There were no associations of COVID-19 severity, reinfection, vaccination frequency, antiviral treatment for COVID-19 with the presence of erectile dysfunction. It was considered that mental support for the subject with erectile dysfunction as a long-COVID symptom is warranted.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".