The Association Between Overactive Bladder and Falls and Fractures: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Urinary symptoms are associated with an increased risk of falls, but few studies have focused on patients with overactive bladder (OAB). This study aimed to synthesize estimates of the risk of falls and fractures in patients with OAB. METHODS: Medline, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and Scopus were systematically searched for observational studies that focused on patients with OAB. When available, data from a non-OAB comparison sample were included. Double independent review and data extraction were performed. Falls and fractures data were summarized by unadjusted and adjusted risks, and percent attributable risk (PAR) of falls and fractures associated with OAB. RESULTS: Fifteen studies were included in the analyses. The proportion of patients with OAB experiencing at least one fall over a year ranged from 18.9% to 50.0%, and the proportion of patients with OAB experiencing recurrent or serious falls ranged from 10.2% to 56.0%. In studies that included a non-OAB comparison sample, a higher risk of falls was observed in patients with OAB compared to those without. A significantly increased (1.3- to 2.3-fold) adjusted OAB-associated risk of falls was reported, while unadjusted PARs for OAB associated falls ranged from 3.7% to 15.5%. Risk was higher among women and those 65 years of age or older. While analysis of fractures showed elevated point estimates, most studies were underpowered to detect a statistically significant difference between groups. CONCLUSIONS: Evidence from the published literature clearly demonstrates the importance of OAB and its symptoms as risk factors for falls and fractures. FUNDING: Astellas.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".