Prevalence of urinary tract infections in women with vulvovaginal atrophy and the impact of vaginal prasterone on the rate of urinary tract infections
Bibliographic record
Abstract
OBJECTIVE: The aims of this study were to assess the prevalence of urinary tract infections (UTI) in women newly diagnosed with vulvovaginal atrophy (VVA) versus women without VVA and to evaluate the potential of vaginal prasterone to be used in postmenopausal VVA women with UTI as prophylaxis to reduce the future UTI risk. As a first subgroup analysis, women using aromatase inhibitors, medications that stop the production of estrogen were analyzed. As a second subgroup analysis, we looked at women with diabetes to investigate whether the same prophylaxis approach should be considered. METHODS: This observational retrospective inception cohort study was conducted using the Integrated Dataverse open-source claims database with data from February 2015 through January 2020. RESULTS: A total of 22,245 women treated with prasterone for a minimum of 12 weeks were matched to women without any prescribed VVA-related treatment. Overall, women treated with prasterone have a significantly lower UTI prevalence compared to those untreated (6.58% vs 12.3%; P < 0.0001). The highest difference in UTI prevalence among the prasterone treated and untreated women was observed in those aged 65-74 (7.15% vs 16.2%; P < 0.0001). Among aromatase inhibitor users and women with diabetes, those treated with prasterone have a significantly lower UTI prevalence (4.90% vs 9.79%; P < 0.01 and 14.59% vs 20.48%; P < 0.0001, respectively). CONCLUSIONS: This study suggests that intravaginal prasterone may be a good candidate for prophylaxis in postmenopausal women with UTI to reduce future UTI risk, including for women taking aromatase inhibitors and women with diabetes. This study is based on real-world evidence and warrants further investigation in a clinical setting.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".