LUTS in Older Adults: Definitions, Comorbidity Impact, Patient Priorities, and Treatment Strategies for Managing Daytime and/or Nighttime Symptoms ‐ ICI‐RS 2024
Bibliographic record
Abstract
INTRODUCTION: Lower urinary tract symptoms (LUTS) are prevalent among older adults, a population that faces multiple medical challenges, with frailty being a major concern. Despite the high prevalence of LUTS, current treatment strategies for older adults are often inadequate. This paper aims to address these issues by defining daytime and/or nighttime LUTS and examining the impact of comorbid conditions on these symptoms. We will identify the priorities of older patients regarding LUTS management and propose treatment strategies to improve outcomes in this vulnerable population. METHODS: This review is based on discussions at the ICI-RS 2024 meeting in Bristol, UK, alongside an extensive literature review examining LUTS in older adults. The review explores distinctions between daytime and nighttime symptoms, the impact of frailty, patient priorities, treatment strategies and the role of comorbidities. RESULTS: LUTS in older adults present a complex and multifaceted challenge, with notable differences between daytime and nighttime manifestations, which may coexist. Clear definitions are needed. There is a lack of evidence that treating associated comorbidities will improve LUTS or urinary incontinence. Personalized care approaches, integrated into broader geriatric health strategies, are essential for addressing these symptoms. An ideal treatment strategy is proposed, focusing on daytime, nighttime or combined day- and nighttime LUTS. Further research is needed to refine treatment pathways and optimize outcomes for this population. CONCLUSIONS: A comprehensive, individualized approach is necessary to address LUTS in older adults. Future research should focus on refining diagnostic definitions, exploring the interplay between comorbidities and LUTS, and developing patient-centered treatment strategies that account for both daytime and nighttime (or combined) symptoms.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".