Determining patient preferences in the treatment of medication‐refractory overactive bladder
Bibliographic record
Abstract
INTRODUCTION: Overactive bladder (OAB) is often suboptimally addressed by behavioral or pharmacological treatments. Less than 15% of patients choose to pursue advanced OAB therapy (sacral nerve stimulation [SNS], percutaneous tibial nerve stimulation [PTNS], and bladder onabotulinum toxin type-A [BTX-A]). We seek to better understand which factors are most important to patients when choosing a third-line therapy. METHODS/MATERIALS: We developed a conjoint analysis survey that included five attributes of the third-line options for OAB (SNS, PTNS, and BTX-A). We administered the survey to new patients with urinary incontinence at two institutions. A hierarchical Bayes random effects regression analysis was used to determine the relative importance of the attributes. A choice simulator was used to model which third-line treatment options patients preferred. We followed patients to see if they pursued their predicted treatment. RESULTS: A total of 108 patients completed the study of whom 89% were women. There was representation from all age groups. The most important attributes of decision-making were the frequency of future procedures, the risk of catheterization, and the need for a device. On market simulation, SNS was the preferred treatment option (47%), followed by PTNS (29%) and BTX-A (14%). Only 10% of patients did not find any treatment option acceptable. CONCLUSIONS: Frequent follow-up, risk of catheterization, and the need for a device were the most important attributes when making a decision on third-line OAB therapy. On market simulation, SNS is the preferred treatment for all age groups though the ultimate choice in third-line therapy may be affected by external factors.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".