The AUA/SUFU guideline on the diagnosis and treatment of idiopathic overactive bladder
Bibliographic record
Abstract
PURPOSE: The purpose of this guideline is to provide evidence-based guidance to clinicians of all specialties on the evaluation, management, and treatment of idiopathic overactive bladder (OAB). The guideline informs the reader on valid diagnostic processes and provides an approach to selecting treatment options for patients with OAB through the shared decision-making process, which will maximize symptom control and quality of life, while minimizing adverse events and burden of disease. METHODS: An electronic search employing OVID was used to systematically search the MEDLINE and EMBASE databases, as well as the Cochrane Library, for systematic reviews and primary studies evaluating diagnosis and treatment of OAB from January 2013 to November 2023. Criteria for inclusion and exclusion of studies were based on the Key Questions and the populations, interventions, comparators, outcomes, timing, types of studies and settings (PICOTS) of interest. Following the study selection process, 159 studies were included and were used to inform evidence-based recommendation statements. RESULTS: This guideline produced 33 statements that cover the evaluation and diagnosis of the patient with symptoms suggestive of OAB; the treatment options for patients with OAB, including Noninvasive therapies, pharmacotherapy, minimally invasive therapies, invasive therapies, and indwelling catheters; and the management of patients with BPH and OAB. CONCLUSION: Once the diagnosis of OAB is made, the clinician and the patient with OAB have a variety of treatment options to choose from and should, through shared decision-making, formulate a personalized treatment approach taking into account evidence-based recommendations as well as patient values and preferences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".