Risk of Hospitalization in Long-Term Care Residents Living with Heart Failure: a Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Older adults living with heart failure (HF) in long-term care (LTC) experience frequent hospitalization. Using routinely available clinical information, we examined resident-level factors that precipitate hospitalization within 90 days of admission to LTC. METHODS: This was a retrospective cohort study of older adults diagnosed with HF, who were admitted to LTC in Ontario, Canada, between 2011 and 2013. Multivariate logistic regression models using generalized estimating equations were developed to determine predictors of hospitalization in residents with HF. RESULTS: Entry to LTC from a hospital was the strongest predictor of future hospitalization (OR: 8.1, 95% CI: 7.1-9.3), followed by a score of three or greater on the Changes in Health, End-stage Signs and Symptoms scale, a measure of moderate to severe medical instability (O.R 4.2, 95% CI: 3.1-5.9). Other variables that increased the likelihood of hospitalization included being flagged as a high risk for falls, two or more physician visits, and increased monitoring for acute medical illness within 14 days of admission. CONCLUSION: Our findings highlight that health instability and transitions from acute to LTC will increase the likelihood of transitioning back into the hospital setting. The identified predisposing factors suggest the need for targeted prevention strategies for those in high-risk groups.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".