Predictors of Sexual Desire Disorders in Women
Bibliographic record
Abstract
INTRODUCTION: A historic belief was that testosterone was the "hormone of desire." However, recent data, which show either minimal or no significant correlation between testosterone levels and women's sexual desire, suggest that nonhormonal variables may play a key role. AIM: To compare women with hypoactive sexual desire disorder (HSDD) and those with the recently proposed more symptomatic desire disorder, Sexual Desire/Interest Disorder (SDID), on the relative contribution of hormonal vs. nonhormonal variables. METHODS: Women with HSDD (N = 58, mean age 52.5) or SDID (N = 52, mean age 50.9) participated in a biopsychosocial assessment in which six nonhormonal domains were evaluated for the degree of involvement in the current low desire complaints. Participants provided a serum sample of hormones analyzed by gas chromatography-mass spectrometry or liquid chromatography/mass spectrometry/mass spectrometry. MAIN OUTCOME MEASURES: Logistic regression was used to assess the ability of variables (nonhormonal: history of sexual abuse, developmental history, psychosexual history, psychiatric status, medical history, and sexual/relationship-related factors; hormonal: dehydroepiandrosterone [DHEA], 5-diol, 4-dione, testosterone, 5-α-dihydrotestosterone, androsterone glucuronide, 3α-diol-3G, 3α-diol-17G, and DHEA-S; and demographic: age, relationship length) to predict group membership. RESULTS: Women with SDID had significantly lower sexual desire and arousal scores, but the groups did not differ on relationship satisfaction or mood. Addition of the hormonal variables to the two demographic variables (age, relationship length) did not significantly increase predictive capability. However, the addition of the six nonhormonal variables to these two sets of predictors significantly increased ability to predict group status. Developmental history, psychiatric history, and psychosexual history added significantly to the predictive capability provided by the basic model when examined individually. CONCLUSIONS: Nonhormonal variables added significant predictive capability to the basic model, highlighting the importance of their assessment clinically where women commonly have SDID in addition to HSDD, and emphasizing the importance of addressing psychological factors in 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".