Erectile Dysfunction: A Primer for in Office Management
Bibliographic record
Abstract
Introduction: Optimizing erectile dysfunction (ED) remains a clinically significant endeavor as insufficient outcomes from oral, injectable and even surgical approaches to treatment remain less than ideal. In this report, we integrate evolving knowledge and provide an algorithmic approach for the clinician to fine-tune management. Methods: We performed a PubMed and Medline search of Erectile Dysfunction treatment optimization, enhanced patient efficacy for ED, and why men fail ED treatment. All relevant papers for the past two decades were reviewed. Results: Establishing the goals and objectives of the patient and partner while providing detailed instructions for treatment can minimize failures and create an environment that allows treatment optimization. A thorough work-up may identify reversible or contributing causes. We identified several areas where treatment of ED could be optimized. These include; management of associated medical conditions, lifestyle improvements, PDE5 inhibitor prescription strategies, management of hypogonadism and the initiation of intracavernosal injection therapy (ICI). Conclusions: In our view, once a man presents for help to the clinician, use of the simple strategies identified in this review to optimize the tolerability, safety and effectiveness of the selected treatment should result in enhanced patient and partner satisfaction, with improved outcomes.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".