Women’s Sexual Desire and Arousal Disorders
Bibliographic record
Abstract
INTRODUCTION: A committee of five was convened to update the chapter on women's sexual dysfunctions from the perspective of diagnostic issues, pathophysiology, assessment, and treatment. AIM: To review the literature since 2003 and provide recommendations based on evidence. METHODS: Research databases, conference proceedings, and articles in press were read for relevant new data on these topics for hypoactive sexual desire disorder (HSDD), female sexual arousal disorder (FSAD), female orgasmic disorder (FOD), and persistent genital arousal disorder (PGAD). MAIN OUTCOME MEASURES: Recommendations by five experts from five countries were formulated with associated grades. RESULTS: The definitions of HSDD, FSAD, and FOD in the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text-Revised are imperfect and have been criticized over the last decade. Proposed new criteria that take into account empirical findings and the diversity across women are recommended. There has been a flurry of new epidemiological studies on women's sexual dysfunction; studies also assessing distress consistently find a much lower prevalence of dysfunction if distress is considered. Assessment of sexual difficulties is best achieved through a biopsychosocial clinical interview of the woman and her partner (if possible); though laboratory investigations, a physical examination, psychophysiological measurement, and self-report questionnaires can often supplement the interview information. There are currently no approved pharmacological treatments for women's sexual dysfunction in North America, though a number of promising agents have been studied. Evidence for the efficacy of psychological treatments is based on limited studies. There is an urgent need for more data on the assessment, etiology, and treatment of PGAD. CONCLUSIONS: Specific recommendations for the assessment and treatment of women's desire, arousal, and orgasm disorders are forwarded; however, more research into these domains is needed.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".