Hearing and Cognitive Impairments Increase the Risk of Long-term Care Admissions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The objective of the study was to understand how sensory impairments, alone or in combination with cognitive impairment (CI), relate to long-term care (LTC) admissions. RESEARCH DESIGN AND METHODS: This retrospective cohort study used existing information from two interRAI assessments; the Resident Assessment Instrument for Home Care (RAI-HC) and the Minimum Data Set 2.0 (MDS 2.0), which were linked at the individual level for 371,696 unique individuals aged 65+ years. The exposure variables of interest included hearing impairment (HI), vision impairment (VI) and dual sensory impairment (DSI) ascertained at participants' most recent RAI-HC assessment. The main outcome was admission to LTC. Survival analysis, using Cox proportional hazards regression models and Kaplan-Meier curves, was used to identify risk factors associated with LTC admissions. Observations were censored if they remained in home care, died or were discharged somewhere other than to LTC. RESULTS: = 0.20). The main risk factor for LTC admission was a diagnosis of Alzheimer's dementia (HR = 1.87; CI: 1.83, 1.90). A significant interaction between HI and CI was found, whereby individuals with HI but no CI had a slightly faster time to admission (40.5 months; HR = 1.14) versus clients with both HI and CI (44.9 months; HR = 2.11). DISCUSSION AND IMPLICATIONS: Although CI increases the risk of LTC admission, HI is also important, making it is imperative to continue to screen for sensory issues among older home care clients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".