The International Society for the Study of Women's Sexual Health Process of Care for Management of Hypoactive Sexual Desire Disorder in Women
Bibliographic record
Abstract
The International Society for the Study of Women's Sexual Health process of care (POC) for management of hypoactive sexual desire disorder (HSDD) algorithm was developed to provide evidence-based guidelines for diagnosis and treatment of HSDD in women by health care professionals. Affecting 10% of adult females, HSDD is associated with negative emotional and psychological states and medical conditions including depression. The algorithm was developed using a modified Delphi method to reach consensus among the 17 international panelists representing multiple disciplines. The POC starts with the health care professional asking about sexual concerns, focusing on issues related to low sexual desire/interest. Diagnosis includes distinguishing between generalized acquired HSDD and other forms of low sexual interest. Biopsychosocial assessment of potentially modifiable factors facilitates initiation of treatment with education, modification of potentially modifiable factors, and, if needed, additional therapeutic intervention: sex therapy, central nervous system agents, and hormonal therapy, guided in part by menopausal status. Sex therapy includes behavior therapy, cognitive behavior therapy, and mindfulness. The only central nervous system agent currently approved by the US Food and Drug Administration (FDA) for HSDD is flibanserin in premenopausal women; use of flibanserin in postmenopausal women with HSDD is supported by data but is not FDA approved. Hormonal therapy includes off-label use of testosterone in postmenopausal women with HSDD, which is supported by data but not FDA approved. The POC incorporates monitoring the progress of therapy. In conclusion, the International Society for the Study of Women's Sexual Health POC for the management of women with HSDD provides a rational, evidence-based guideline for health care professionals to manage patients with appropriate assessments and individualized treatments.
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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 | 0.057 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".