Impact of Baseline Total Testosterone Level on Successful Treatment of Sexual Dysfunction in Men Taking Once-Daily Tadalafil 5 mg for Lower Urinary Tract Symptoms and Benign Prostatic Hyperplasia: An Integrated Analysis of Three Randomized Controlled Trials
Bibliographic record
Abstract
INTRODUCTION: Controversy exists as to whether erectile response to phosphodiesterase type 5 inhibitors is compromised in men with low total testosterone (TT) levels. This is amplified by reports of improved response to phosphodiesterase type 5 inhibitor therapy after coadministration of testosterone replacement therapy in hypogonadal men unresponsive to phosphodiesterase type 5 inhibitors. AIM: To determine whether TT and luteinizing hormone levels influence efficacy of tadalafil for erectile dysfunction in men with concomitant lower urinary tract symptoms and benign prostatic hyperplasia. METHODS: This integrated analysis included 1,075 men randomized to once-daily tadalafil 5 mg (n = 540) or placebo (n = 535) for 12 weeks in three prospective clinical trials who had not received concomitant testosterone replacement therapy. Subjects were categorized at baseline by low vs normal TT levels (n = 1,049; <300 vs ≥300 ng/dL) and normal vs high luteinizing hormone levels (n = 1,058; ≤9.4 vs >9.4 mIU/mL). Treatment-group differences in International Index of Erectile Function (IIEF) by hormone subgroups were assessed using analysis of covariance. MAIN OUTCOME MEASURES: Changes in IIEF erectile function domain and other domain scores. RESULTS: The overall study population was comprised primarily of white men (>86%) with a mean age range of 64 to 70 years. Median baseline TT level in the integrated population was 355 ng/dL; levels were lower than 300 ng/dL (cutoff for normal) in 32.4% of men. Men with low TT levels reported diabetes (21.8%), cardiovascular disease (54.1%), and hypertension (49.1%) numerically more often than men with normal TT levels (10.6%, 43.2%, and 36.7%, respectively). Low TT and high luteinizing hormone levels were associated with numerically, but not statistically significantly, lower 12-week IIEF domain scores compared with those with normal levels. Changes in most 12-week IIEF domain scores showed that tadalafil was significantly more effective than placebo (P < .02). CONCLUSION: Low TT levels at baseline did not negatively influence response to tadalafil in men of advancing age with concomitant lower urinary tract symptoms and benign prostatic hyperplasia and erectile dysfunction.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".