Integrating partners into erectile dysfunction treatment: improving the sexual experience for the couple
Bibliographic record
Abstract
INTRODUCTION: Erectile dysfunction (ED) is a common condition estimated to affect more than 150 million men worldwide. ED should be regarded as a shared sexual problem which has significant detrimental effects both on the men who experience this condition and on their partners. EVIDENCE TO SUPPORT PARTNER INVOLVEMENT IN ED THERAPY: Evidence shows that the partner plays a key supportive role in the man's ED treatment and in successful long-term ED therapy. Including the partner in consultations may highlight discordant attitudes and communication problems between couple members which may indicate treatment acceptance or rejection, or realistic or unrealistic treatment expectations. OPTIONS FOR PARTNER INVOLVEMENT IN ED THERAPY: Most patients with ED consult their physician in the absence of their partner. Therefore, involving the partner in therapy can be challenging. Two options which physicians should consider are: encouraging the patient to bring the partner into the office and, often more realistically, seeking information about, and providing information to, the partner, via the patient. OBJECTIVES: The objective of these recommendations is to provide practical guidance on treating couples affected by ED, and suggest techniques that may be helpful in integrating the partner into the process of ED 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".