Female sexual dysfunction in androgenetic alopecia: Case-control study
Bibliographic record
Abstract
INTRODUCTION: We sought to evaluate the association of female sexual dysfunction (FSD) with androgenetic alopecia (AGA) and metabolic syndrome (MetS) in premenopausal women. METHODS: From December 2013 to June 2015, we performed a case-control, prospective study of 115 patients with AGA and 97 age-matched control patients without AGA from among premenopausal women who visited dermatology clinics of the two reference hospitals. Comprehensive history, anthropometric measurements, and questionnaire administration were performed for each of the total of 212 women. The Female Sexual Function Index (FSFI) was used to assess the key dimensions of female sexual function. AGA was assessed and graded by an experienced dermatologist according to Ludwig's classification. The MetS assessment was made according to the NCEP-ATP III criteria. RESULTS: In univariate analysis, age, weight, waist circumference, hip circumference, waist-to-hip ratio, body mass index (BMI), AGA, MetS, cardiovascular event, marital status, hypertension, high fasting plasma glucose, high triglyceride, large waist, total testosterone, and free testosterone were associated with presence of FSD. In logistic regression analysis, age (odds ratio [OR] 1.21, 95% confidence interval [CI] 1.13-1.30; p<0.001), AGA (OR 3.42, 95% CI 1.31-8.94; p=0.017), MetS (OR 5.39, 95% CI 1.34-21.62; p=0.012), and free testosterone (OR 0.18, 95% CI 0.09-0.37; p<0.001) were independently associated with FSD. CONCLUSIONS: Our study suggests that age, AGA, MetS, and free testosterone may have strong impact on sexual function in premenopausal women. Further studies with population-based and longitudinal design should be conducted to confirm this finding.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".