Falls risk reduction and treatment of overactive bladder symptoms with antimuscarinic agents: A scoping review
Bibliographic record
Abstract
AIMS: Overactive bladder (OAB) symptoms are associated with falls and fractures in older adults and treatment with antimuscarinic agents may decrease this falls risk. Bladder-specific antimuscarinic agents may also adversely affect falls risk because of drug-related cognitive impairment. Thus, a tension between effective treatment, falls risk reduction, and increased falls risk is created. We conducted a scoping review to determine whether sufficient studies exist to warrant a full systematic review of falls risk reduction through treatment of OAB and to identify gaps in current research. METHODS: Using an iterative scoping approach, a search of electronic databases was undertaken using key terms. Studies in any setting of older adults who had fallen or were at risk for falls and were provided pharmacological treatment of OAB to reduce of falls or falls risk were sought. Relevant articles were identified, reviewed, and used to map research activity regarding the pharmacological treatment of OAB in older adults and its relationship to falls and falls risk reduction. RESULTS: Only one study met our initial inclusion criteria. Six additional studies were useful in identification research gaps, particularly in terms of outcome measures. CONCLUSIONS: Insufficient evidence exists to recommend antimuscarinic treatment as a strategy for falls reduction, and the contribution of such agents to increased falls risk is unclear. Future studies of antimuscarinic agents for OAB must include measures of falls and falls risk and cognitive effects.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".