Women’s Sexual Pain Disorders
Bibliographic record
Abstract
INTRODUCTION: Women's sexual pain disorders include dyspareunia and vaginismus and there is need for state-of-the-art information in this area. AIM: To update the scientific evidence published in 2004, from the 2nd International Consultation on Sexual Medicine pertaining to the diagnosis and treatment of women's sexual pain disorders. METHODS: An expert committee, invited from six countries by the 3rd International Consultation, was comprised of eight researchers and clinicians from biological and social science disciplines, for the purpose of reviewing and grading the scientific evidence on nosology, etiology, diagnosis, and treatment of women's sexual pain disorders. MAIN OUTCOME MEASURE: Expert opinion was based on grading of evidence-based medical literature, extensive internal committee discussion, public presentation, and debate. Results. A comprehensive assessment of medical, sexual, and psychosocial history is recommended for diagnosis and management. Indications for general and focused pelvic genital examination are identified. Evidence-based recommendations for assessment of women's sexual pain disorders are reviewed. An evidence-based approach to management of these disorders is provided. CONCLUSIONS: Continued efforts are warranted to conduct research and scientific reporting on the optimal assessment and management of women's sexual pain disorders, including multidisciplinary approaches.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.006 | 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".