P.001 Predictors of long-term care admission in patients presenting to the rural and remote memory clinic in Saskatchewan
Bibliographic record
Abstract
Background: Transitioning from home to long-term care (LTC) is challenging for people with dementia and their caregivers. We aimed to elucidate factors predicting long-term care admission within two years of presentation to the Rural and Remote Memory Clinic (RRMC) in Western Canada. Methods: A total of 679 community-dwelling patients were seen at the RRMC in Saskatchewan between its establishment in March 2004 through June 2019. Data analysis included 635 patients (admitted to LTC within two years = 222, not admitted to LTC = 413). Each patient was assessed neuropsychologically and completed self-report questionnaires measuring several domains. Both groups were compared using logistic regression analyses. Results: Univariate logistic regressions showed that age (OR = 1.052, CI = 1.035-1.069), male sex (OR = 1.794, CI = 1.279-2.517), Functional Activities Questionnaire (OR = 1.085, CI = 1.057-1.114), MMSE (OR = 0.861, CI = 0.827-0.897), and Clinical Dementia Rating score (OR = 1.132, CI = 1.062-1.206) remained significant (p < .001). Preserved cognition, as measured by the MMSE, was protective. Conclusions: We found that being older, male, more dependent in activities of daily living, and having increased severity of dementia predicted LTC admission. This information may help in planning care for individuals with dementia.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".