Phosphodiesterase Type 5 Inhibitors for the Management of Erectile Dysfunction: Preference and Adherence to Treatment
Bibliographic record
Abstract
Erectile dysfunction (ED) is a common medical condition that has a negative impact on men and their partners. The field has revolutionised over the last two decades and more treatment options are available now for the treatment of ED than ever before. Among available treatment options, the most commonly prescribed therapies are oral phosphodiesterase type 5 (PDE5) inhibitors. The first drug in this class, sildenafil citrate, generally provides patients and their partners with efficacious, safe, and discreet treatment that rapidly has become the first-line treatment option. Its successful introduction into clinical practice was soon followed by the launch of two other PDE5 inhibitors: tadalafil and vardenafil. The existence of these drugs has resulted in an increase in their marketing. However, the abundance of choices made the question "which PDE-5 inhibitor?" relevant for clinicians, patients and their partners. It is widely accepted that there are no significant differences in their safety and efficacy, a fact that has led to the initiation of studies aiming to evaluate them regarding patient preference. Nevertheless, the results are rather conflicting. Also a significant percentage of men initiating treatment switch between inhibitors or discontinue therapy. This article examines the peer-reviewed published data addressing patient's preference and adherence to ED treatment with PDE5 inhibitors. It also examines strategies to improve compliance and satisfaction with treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".