Assessment and management of sexual dysfunction in the context of depression
Bibliographic record
Abstract
Sexual dysfunction (SD) is pervasive and underreported, and its effects on quality of life are underestimated. Due in part to its bidirectional relationship with depression, SD can be difficult to diagnose; it is also a common side effect of many antidepressants, leading to treatment noncompliance. While physicians often count on patients to spontaneously report SD, treatment is optimized when the clinician instead performs a thorough assessment of sexual functioning before and during drug therapy using a standardized questionnaire such as the Arizona Sexual Experiences Scale (ASEX). Separating the effects of the disorder from those of medications is challenging; we present a concise, evidence-based schematic to assist physicians in minimizing treatment-emergent sexual dysfunction (TESD) while treating depression. Vascular, hormonal, neurogenic, and pharmacological factors should be considered when a patient presents with SD. We also recommend that physicians obtain patient information about baseline and historical sexual functioning before prescribing a drug that may lead to SD and follow up accordingly. When the goal is to treat depression while attenuating the risk of sexual symptoms, physicians may wish to consider agomelatine, bupropion, desvenlafaxine, moclobemide, trazodone, vilazodone, and vortioxetine.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".