The Impact of Patient Navigators on Overactive Bladder Care: Real-World Practice Patterns from a US National Database
Bibliographic record
Abstract
Background/Objectives: We here describe the impact of navigated care on utilization patterns of pharmacologic and minimally invasive overactive bladder therapies. Methods: This retrospective observational cohort study used electronic medical record data from the Precision Point Specialty Analytics Portal in the United States. Eligible patients were adults (≥18 years) newly diagnosed and treated for non-neurogenic overactive bladder (1 January 2015 to 31 December 2019). Categorical endpoints were analyzed by chi-square test or Fisher exact test. Of 170,000 eligible patients, 8982 (≈5%) were randomly selected and stratified by navigation status (navigated: 1150 [12.8%]; non-navigated: 7832 [87.2%]). Results: Overall, 60.0% of patients were female, 69.9% were White, and 42.7% had Medicare coverage. Navigated care was more common among women, Black patients, and those covered by Medicaid/Medicare. Initial pharmacologic treatment rates were similar between navigated and non-navigated groups (anticholinergic: 57.0% vs. 57.4%; beta-3 agonist: 43.0% vs. 42.6%). Greater percentages of navigated versus non-navigated patients received minimally invasive therapy (23.8% vs. 10.8%, respectively; p < 0.0001). Discontinuation rates were lower for navigated versus non-navigated patients undergoing pharmacologic treatment (62.5% vs. 71.3%; p < 0.0001). Conclusions: Patient navigation for overactive bladder may help increase access to minimally invasive therapies and may be a tool to address treatment disparities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".