Methodological Challenges in Studying Testosterone Therapies for Hypoactive Sexual Desire Disorder in Women
Bibliographic record
Abstract
INTRODUCTION: Testosterone has been studied for its benefits on sexual health for decades. The research regarding testosterone in women has produced evidence that this is a potential treatment for women suffering from female sexual dysfunction. There are several limitations of the testosterone trials that can affect their interpretation and challenges posed by some regulatory agencies that have prevented approval of any testosterone treatment for women in several countries. AIM: To summarize the challenges of testosterone trials in terms of study populations, patient-reported outcomes, validated instruments in research, confounders, and regulatory barriers. METHODS: A thorough review of published data on testosterone for the treatment of women's sexual health problems was undertaken. A detailed evaluation of the limitations of these trials was conducted and incorporated with the published evidence on the regulatory processes involved in moving testosterone from clinical research to drug approval. MAIN OUTCOME MEASURE: Main outcome measures are assessment of clinical trial populations, survey tools, confounders, and regulatory barriers. RESULTS: There is some heterogeneity of study populations included in testosterone trials in women. Similarly, there have been differences in instruments used to assess patient-reported outcomes and often minimal control for potential confounders. The regulatory agency had posed a challenge to approve any testosterone treatment for women based on unproven concerns and a lack of regulatory guidance for drug developers. CLINICAL IMPLICATIONS: There is strong evidence that shows testosterone is effective for treating sexual health concerns in the women included in clinical trials. STRENGTH & LIMITATIONS: Strengths include thorough review of published literature and trial design for sexual health concerns. Limitations include being restricted to English Language publications and not having access to unpublished clinical trial data. CONCLUSIONS: Testosterone trials in women have been limited by homogeneity in the study populations and outcomes measured. Drug development has been hampered by inconsistent regulatory barriers. Rowen TS, Davis SR, Parish S, et al. Methodological Challenges in Studying Testosterone Therapies for Hypoactive Sexual Desire Disorder in Women. J Sex Med 2020;17:585-594.
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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 | 0.372 | 0.574 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".