Clinical Characteristics Associated With Unsuccessful Pessary Fitting Outcomes
Bibliographic record
Abstract
OBJECTIVES: To identify clinical characteristics and quality of life/symptom questionnaire scores associated with unsuccessful vaginal pessary trials in the treatment of pelvic organ prolapse (POP). METHODS: This was a retrospective study of pessary fittings between 2006 and 2012 at our tertiary care urogynecology unit. One hundred one patients with symptomatic POP filled out detailed history and validated pelvic floor quality-of-life and symptom questionnaires at baseline. They were examined and POP was staged. After discussion of treatment options, they agreed to attempt a trial of pessary (TOP). Unsuccessful TOP was defined as an inability to continue pessary use beyond 4 weeks from initial fitting. Stepwise logistic regression analysis was performed to build a prediction model for the odds of unsuccessful TOP. RESULTS: The main reason for unsuccessful TOP was patient discomfort. Multivariate stepwise logistic regression showed that age 65 or younger (odds ratio [OR], 3.13; P = 0.042), smoking history (OR, 3.42; P = 0.049), genital hiatus/total vaginal length ratio greater than 0.8 (OR, 6.70; P = 0.042), and lower POP Quantification overall stage (OR, 2.84; P = 0.017) were associated with increased likelihood of unsuccessful TOP. Other variables such as sexual activity and concurrent urinary or POP symptoms did not affect the outcome. CONCLUSIONS: Several clinical characteristics influenced the likelihood of unsuccessful TOP. These may be taken into account for clinical counseling. Pessaries remain a good treatment option, as many clinical variables did not seem to influence the success of fittings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".