Evolving therapeutic strategies for premature ejaculation: The search for on-demand treatment – topical versus systemic
Bibliographic record
Abstract
Premature ejaculation (PE) is a common sexual dysfunction affecting20% to 30% of men worldwide. Definitions of PE vary, but itis typically characterized by short intravaginal ejaculatory latencytime (IELT) with concomitant sexual dissatisfaction and distress.PE may be lifelong or acquired, but its etiology remains unclear.Treatment of PE typically involves pharmacotherapy, particularlywhen lifelong. Although there are numerous reports on the offlabeluse of selective serotonin reuptake inhibitors (SSRIs) andother compounds, only 2 treatments have been evaluated in randomizedcontrolled phase 3 clinical trials: PSD502 and dapoxetine(SSRI). Both significantly improved IELT and patient-reportedoutcome domains of ejaculatory control, sexual satisfaction, anddistress as measured by the index of premature ejaculation (IPE),compared with placebo. They constitute the focus of this review.Evidence demonstrated that PSD502, dapoxetine and other SSRIsall significantly improve the symptoms of PE. Systemic use of SSRIspresents risks associated with the known pharmacology of thisclass. PSD502 allows for topical on-demand treatment appliedapplied immediately before intercourse, and is not associated withsystemic adverse events.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".