Predictors of improvement in urinary incontinence in the postacute setting: A Canadian cohort study
Bibliographic record
Abstract
PURPOSE: To determine factors associated with improvement in urinary incontinence (UI) for long-stay postacute, complex continuing care (CCC) patients. DESIGN: A retrospective cohort investigation of patients in a CCC setting using data obtained from the Canadian Institute for Health Information's Continuing Care Reporting System collected with interRAI Minimum Data Set 2.0. SETTING AND PARTICIPANTS: Individuals aged 18 years and older, were admitted to CCC hospitals in Ontario, Canada, between 2010 and 2018. METHODS: Multivariable logistic regression was used to determine the independent effects of predictors on UI improvement, for patients who were somewhat or completely incontinent on admission and therefore had the potential for improvement. RESULTS: The study cohort consisted of 18 584 patients, 74% (13 779) of which were somewhat or completely incontinent upon admission. Among those patients with potential for improvement, receiving bladder training, starting a new medication 90 days prior (odds ratio, OR: 1.54 [95% confidence interval, CI: 1.36-1.75]), and triggering the interRAI Urinary Incontinence Clinical Assessment Protocol to facilitate improvement (OR: 1.36 [95% CI: 1.08-1.71]) or to prevent decline (OR: 1.32 [95% CI: 1.13-1.53]) were the strongest predictors of improvement. Conversely, being totally dependent on others for transfer (OR: 0.62 [95% CI: 0.42-0.92]), is rarely or never understood (OR: 0.65 [95% CI: 0.50-0.85]), having a major comorbidity count of ≥3 (OR: 0.72 [95% CI: 0.59-0.88]), Parkinson's disease, OR: 0.77 (95% CI: 0.62-0.95), Alzheimer/other dementia, OR: 0.83 (95% CI: 0.74-0.93), and respiratory infections, OR: 0.57 (95% CI: 0.39-0.85) independently predicted less likelihood of improvement in UI. CONCLUSIONS AND IMPLICATIONS: Findings of this study suggest that improving physical function, including bed mobility, and providing bladder retraining have strong positive impacts on improvement in UI for postacute care patients. Evidence generated from this study provides useful care planning information for care providers in identifying patients and targeting the care that may lead to better success with the management of UI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".