β3‐Adrenoceptor Agonists for Neurogenic Lower Urinary Tract Dysfunction: Evidence and Clinical Rationale for First‐Line Therapy
Bibliographic record
Abstract
AIMS: To summarize current evidence on β3-adrenoceptor agonists for managing neurogenic lower urinary tract dysfunction (NLUTD), focusing on their efficacy, safety, and clinical role in optimizing bladder storage and protecting upper tracts. METHODS: Evidence from randomized controlled trials, meta-analyses, and observational studies in spinal cord injury (SCI), multiple sclerosis (MS), and spina bifida populations was reviewed. Outcomes included bladder storage parameters, patient-reported measures, and cardiovascular and cognitive safety profiles. RESULTS: β3-adrenoceptor agonists such as Mirabegron improve cystometric capacity, reduce detrusor pressure, and enhance quality of life. A recent individual-patient meta-analysis confirmed these benefits. Early data on Vibegron in SCI and spina bifida show increased bladder compliance and capacity without new safety concerns. Cardiovascular safety signals are reassuring: one randomized trial in SCI/MS and a large multinational cohort both showed no excess risk. Observational studies continue to associate antimuscarinics with cognitive adverse effects, whereas β3-agonists demonstrate a more favorable tolerability profile. CONCLUSIONS: β3-adrenoceptor agonists represent a safe, effective, and well-tolerated first-line option in NLUTD management. They preserve bladder safety and continence with fewer systemic side effects. OnabotulinumtoxinA remains an important escalation therapy for refractory cases but carries higher procedural and retention risks. A β3-agonist-based strategy offers a rational, patient-centered foundation for treatment.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".