Sildenafil Citrate for Treatment of Erectile Dysfunction in Men With Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: In the 5-10% of diabetic men with type 1 diabetes, erectile dysfunction (ED) may be a particularly common and unwanted complication. This is the first study focusing exclusively on the effects of sildenafil in men with type 1 diabetes and ED. RESEARCH DESIGN AND METHODS: A total of 188 patients were entered into a double-blind, placebo-controlled, parallel-group, flexible-dose study and were randomized to receive sildenafil (25-100 mg; n = 95) or placebo (n = 93) for 12 weeks. Efficacy was evaluated using questions three (Q3; achieving an erection) and four (Q4; maintaining an erection) from the International Index of Erectile Function (IIEF), a global efficacy question (GEQ; "Did treatment improve your erections?"), and a patient event log of sexual activity. RESULTS: Improvements in mean scores from baseline to end-of-treatment for IIEF Q3 (35.7 vs. 19.9%) and Q4 (68.4 vs. 26.5%) were significant in patients receiving sildenafil compared with those receiving placebo (P = 0.0001). Moreover, the percent of improved erections (GEQ, 66.6 vs. 28.6%) and successful intercourse attempts (63 vs. 33%) was significantly increased with sildenafil compared with placebo. Improvements in sexual function were seen irrespective of the degree of ED severity. Adverse events were generally mild to moderate in severity, with headache (20 vs. 8%), flushing (18 vs. 3%), and dyspepsia (8 vs. 1%) reported more often in the sildenafil than in placebo-treated patients. CONCLUSIONS: Treatment with sildenafil for ED was effective, resulting in an increased percentage of successful attempts at intercourse, and was well tolerated among men with type 1 diabetes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".