Vaginal Laser Therapy for Stress Urinary Incontinence: A Systematic Review of Prospective Randomized Clinical Trials
Bibliographic record
Abstract
The most common type of urinary incontinence in women is stress urinary incontinence (SUI) which negatively impacts several aspects of life. The newly introduced vaginal laser therapy is being considered for treating SUI. This systematic review aimed to evaluate the efficacy of vaginal laser therapy for stress urinary incontinence in menopausal women. We searched the following databases: MEDLINE (via PubMed), EMBASE, Cochrane Library databases, Web of Science, clinical trial registry platforms, and Google Scholar, using the MeSH terms and keywords [Urinary Incontinence, Stress] and [(lasers) OR laser]. In our systematic review, prospective randomized clinical studies on women diagnosed with SUI as per the International Continence Society's diagnostic criteria were included. The Cochrane Risk-of-Bias assessment tool for randomized clinical trials was used to evaluate the quality of studies. A total of 256 relevant records in literature databases and registers and 25 in additional searches were found. Following a review of the titles, abstracts, and full texts, four studies involving 431 patients were included. Three studies used CO2-lasers, and one used Erbium: YAG-laser. The results of all four studies revealed the short-term improvement of SUI following both the Erbium: YAG-laser and CO2-laser therapy. SUI treatment with CO2-laser and Erbium: YAG-laser therapy is a quick, intuitive, well-tolerated procedure that successfully improves incontinence-related symptoms. The long-term impact of such interventions has not been well established as most trials focused on the short-term effects.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.013 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".