Efficacy and safety of sertraline for the treatment of premature ejaculation
Bibliographic record
Abstract
BACKGROUD: Evidence on the efficacy and safety of sertraline in patients with premature ejaculation (PE) was inconsistent. The objective of this article is to evaluate the efficacy and safety of sertraline for the treatment of PE. METHODS: We searched Medline (OVID), Embase, the Cochrane Library, and 2 Chinese databases for randomized controlled trials (RCTs) and randomized crossover trials (RTs) that evaluated the efficacy and safety of sertraline in patients with PE. A meta-analysis was performed to calculate their pooled estimates with 95% confidence interval. RESULTS: Of the 645 records obtained, we included 12 RCTs and 2 RTs (n = 977). Meta-analysis showed that sertraline prolonged intravaginal ejaculation latency time (IELT) in PE patients ((standard mean difference (SMD) = 2.14, 95% CI 1.20 to 3.08). Subgroup analyses indicated a prolonged IELT for different treatment courses: 4 weeks (SMD = 2.66, 1.06 to 4.26), 6 weeks (SMD = 0.95, 0.31 to 1.58), and 8 weeks (SMD = 1.81, 0.78 to 2.85). The sexual satisfaction rates of patients (SMD = 2.20, 1.57 to 2.84) and spouses (SMD = 2.27, 1.44 to 3.09) were also improved. We observed a significant increased risk of gastrointestinal upset (risk ratio = 2.71, 1.39 to 5.28) in the sertraline group. CONCLUSION: Sertraline can prolong IELT of PE patients, improve sexual satisfaction rates of patients and spouses, but increase risk of gastrointestinal upset.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".