Effects of Tadalafil on Erectile Dysfunction in Men With Diabetes
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy and safety of tadalafil taken as needed before sexual activity by men with diabetes and erectile dysfunction (ED). RESEARCH DESIGN AND METHODS: Men with type 1 or type 2 diabetes and a minimum 3-month history of ED were randomly allocated to one of three groups: placebo (n = 71), tadalafil 10 mg (n = 73), or tadalafil 20 mg (n = 72) taken up to once daily for 12 weeks. Changes from baseline in mean scores on the erectile function domain of the International Index of Erectile Function (IIEF) and changes from baseline in the proportion of "yes" responses to question 2, "Were you able to penetrate?," and 3, "Were you able to complete intercourse?," of the Sexual Encounter Profile were coprimary outcome measures. RESULTS: A total of 191 (88%) of 216 patients completed the study. Treatment with tadalafil significantly improved all primary efficacy variables, regardless of baseline HbA(1c) level. Therapy with tadalafil also significantly improved a number of secondary outcome measures, including changes in other IIEF domains, individual IIEF questions, and percentage of positive responses to a global assessment question measuring erection improvement. Treatment with tadalafil did not alter mean HbA(1c) levels. Tadalafil was well tolerated, with headache and dyspepsia being the most frequent adverse events with active treatment. CONCLUSIONS: Tadalafil therapy significantly enhanced erectile function and was well tolerated by men with diabetes and ED.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".