Cognitive behavior therapy for female sexual dysfunction: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Sexual dysfunction has a negative influence on both human physical and psychological health across various ages and frequently results in the deterioration of quality of life for individuals and/or partners. OBJECTIVE: The objective of the study was to assess the effectiveness of cognitive behavioral therapy (CBT) for female sexual dysfunction (FSD). METHODS: We searched PubMed, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, and Web of Science databases from inception to January 6, 2023 (updated on April 15, 2024). The risk of bias in all included randomized controlled trials (RCTs) was assessed using the Cochrane risk of bias tool (revised version 2.0), and meta-analysis was conducted using R (version 4.2.0). We used the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach to evaluate the certainty of the evidence. RESULTS: Ten RCTs involving 837 patients were included, and three RCTs were judged at high risk of bias due to missing outcome data and baseline imbalances. In the post-intervention follow-up, CBT participants showed a greater increase in FSFI scores than those receiving routine care (MD: 7.63, 95% CI: 5.25 to 10.02, GRADE: low), and greater improvement than waitlist participants (MD: 3.13, 95% CI: 0.90 to 5.35, GRADE: moderate). In the short-term follow-up (4 to 24 weeks after completion of intervention), CBT participants had a greater increase in FSFI scores than routine care (MD: 11.13, 95% CI: 0.27 to 22.00, GRADE: low) and waitlist participants (MD: 3.80, 95% CI: 1.46 to 6.14, GRADE: very low). CONCLUSION: CBT can improve the clinical symptoms of patients with FSD. However, large-scale RCTs are needed in the future.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".