Examining pessary use and satisfaction in managing pelvic organ prolapse: results from a cross-sectional multicentre patient survey
Bibliographic record
Abstract
BACKGROUND: Vaginal pessaries are a common method of managing pelvic organ prolapse (POP), as well as different types of urinary incontinence, allowing patients to successfully improve overall quality of life. Yet despite their positive attributes, there are several reasons why patients may choose to discontinue using pessaries and proceed with surgery to treat their condition instead. This study aimed to explore patients' experiences of pessary use in treating POP. METHODS: Participants completed an online survey regarding pessary use and ideal characteristics of a pessary. Participants were recruited from social media advertisements, online support groups for women's health-related conditions, and pelvic floor clinics. RESULTS: A total of 100 participants were recruited, of which 77 fully completed the survey. The biggest age group of participants was above 65 years, with 48.1% of participants falling into this category, followed by 35-44 years accounting for 20.8% of respondents. Respondents cited pelvic pain (35.2%), excess vaginal discharge and odor (32.4%), as well as difficulty with pessary placement as the most common issues related to pessary use (41.9%). Easy insertion, removal (81.8%), and relief from side effects (81.8%) were the most commonly reported ideal characteristics for pessary use. CONCLUSION: Patients had important concerns with pessary use and a high number either stopped or were considering stopping even when it improved their POP. Whilst pessaries can help in the management of POP, further improvement is warranted to increase pessary use, such as through the development of user-friendly designs, or applicators to aid with fitting.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".