CO2 Laser Therapy for Genitourinary Syndrome of Menopause in Women with Breast Cancer: A Randomized, Sham-Controlled Trial
Bibliographic record
Abstract
Objectives: We aimed to study the efficacy of fractional CO2 laser for genitourinary syndrome of menopause (GSM) symptoms in breast cancer (BC) survivors through a randomized, sham-controlled study, followed by an open-phase study assessing the impact of additional treatments. Methods: BC survivors with GSM were randomized to receive either three sessions of intravaginal CO2 laser or sham treatment every 3–4 weeks. The laser’s energy was 45–60 mJ/pixel. Outcomes were compared one month following the last session. Participants initially receiving laser treatment were offered three more sessions, while those receiving sham had six laser sessions in an open-label study. Results: Thirty-four BC survivors were randomized to laser (n = 19) or sham (n = 15) treatments. Dyspareunia and intercourse dryness scores improved in both groups one month post-treatment, without a significant advantage of laser over sham. The laser treatment resulted in a reduction in daily dryness (−1.30 ± 0.55, p = 0.017), an increase in vaginal hydration (3.24 ± 1.13, p = 0.004), and an increase in Vaginal Health Index (VHI) (2.26 ± 0.50, p < 0.001). Most participants (18/19 and 9/15, respectively) opted to continue laser treatments after unblinding, resulting in 27 patients receiving six laser treatments. Increasing the number of laser treatments was associated with a constant improvement in Visual Analogue Score (VAS) scores for dyspareunia, intercourse dryness, daily dryness, burning, discomfort, itch, and average VAS, as well as pH, VHI, and hydration. Conclusions Three fractional CO2 laser treatments for BC survivors reduced daily dryness but did not improve dyspareunia and sexual dryness when compared to sham in this randomized trial. Increasing the number of treatment sessions seemed to improve outcomes; however, it remained clinically insufficient, even after six treatments.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".