(52) PLATELET-RICH PLASMA FOR FEMALE SEXUAL DYSFUNCTION AND STRESS URINARY INCONTINENCE: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Abstract Objectives To study the efficacy and safety of PRP injections in women with the above pathologies. Methods A systematic search on PubMed, Embase and the Cochrane Library database was performed, as well as sources of grey literature until January 2023. After title/abstract and full-text screening, clinical studies on humans evaluating the efficacy of PRP in gynecological disorders using standardized tools were included. Risk of bias was undertaken with RoB-2 for randomized-controlled trials (RCT) and the Newcastle-Ottawa Scale (NOS) for observational studies. Results Four prospective and one retrospective study explored FSD, while six prospective and one RCT evaluated female SUI. A total of 327 women with a mean age of 51±12 years were included. For FSD, PRP significantly improved the Female Sexual Function Index (FSFI), the Vaginal Health Index (VHI) and the Female Sexual Distress score (FSDS). For SUI, PRP led to a significant improvement of the International Consultation on Incontinence Questionnaire – Short Form (ICIQ-SF), and the Urogenital Distress Inventory (UDI-6). The identified RCT reported a significantly higher mean score of ICIQ-SF (p<0.05) and UDI-6 (p<0.01) in the midurethral sling group compared to the PRP injections group. Regarding risk of bias, the RCT was characterized by high-risk, whereas the observational studies were of moderate risk. Conclusions PRP injections should be further explored for the treatment of FSD and female SUI. Despite promising initial results, the level of current evidence is low due to methodological issues in the available studies. It becomes clear that there is an emerging need for high-quality research. Conflicts of Interest None.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".