Patterns of Transient and Terminal Transitions in Activities of Daily Living Performance Levels among Long-Term Care Residents: A Multistate Markov’s Model Analysis of Population-Based Longitudinal Data in Canada
Bibliographic record
Abstract
OBJECTIVE: We examined how long-term care (LTC) home residents transition between different activities of daily living (ADL) performance levels, and to eventual terminal clinical outcomes. DESIGN: We conducted a longitudinal retrospective analysis of population-based data among institutionalized older adults within 3 Canadian provinces. SETTING AND PARTICIPANTS: LTC home residents within 3 Canadian provinces of Alberta, British Columbia, and Ontario placed between January 2010 and December 2020. METHODS: We fit a Markov-chain multistate transition model to the data to obtain transition probabilities, sojourn times, as well as the adjusted odds of each transition. RESULTS: Three distinct transitions were commonly experienced by residents from this analysis. Most LTC residents stayed unchanged in their ADL performance level between 90-day assessments, a substantial proportion transitioned to worse performance level, and only a small proportion improved to a better performance level. Residents spent on average between 21 and 29 months on admission before finally transitioning out of the setting to 1 of 4 terminal states that include mortality, hospitalization, home, or other setting discharges. Within 5 years of admission, between 63% and 72% died, 18% to 19% were hospitalized, and 2% to 4% were discharged back home. The odds of transitioning to different states were strongly affected by factors such as Index of Social Engagement, Cognitive Performance Scale, Changes in Health, End Stage Disease, and Signs and Symptoms score, age, as well as province where the LTC home is located, but varied depending on the admission ADL status. CONCLUSIONS AND IMPLICATIONS: Evidence from this study shows that it does not always have to be one way out for LTC residents. LTC home administrators could use the findings to identify residents who could be provided the right intervention to facilitate ADL performance improvement and prevent further decline.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".