Multistate Competing Risk Analysis of Transition Back to the Community Among Long-Term Care Home (LTC) Destined Patients: A Brief Report
Bibliographic record
Abstract
OBJECTIVE: The demand for long-term care in community and facilitybased settings in Canada is expected to increase with population growth. The Toronto Grace Health Center piloted an intervention program that aims to support return to the community of acute hospital patients designated for LTC placement. We investigated whether this program was effective in transitioning the program patients back to their homes in the community and the factors associated with transitioning patients to different destinations. METHOD: We performed a competing risk multi-state analysis of 111 patients enrolled into the Harbour Light (HL) transitional unit program between January 2020 and June 2023. RESULTS: At the time of the study, 92 enrolled patients had been discharged and of those these, 48.9% (45) were successfully transitioned back to their private home in the community. The remaining 51.1% (46) were discharged to other destinations. Being a female was the only positive predictor of transitioning back home. Higher CPS scores (HR 0.53, 95% CI 0.31-0.88), PADDRS scale of 1+, and higher ADL Hierarchy scale, strongly predicted lower odds of transitioning back to the community. CONCLUSION: Within the context of rising LTC bed demand and lengthy waiting time in Canada, with appropriate measures, this program successfully transitioned LTC home bound persons back to their homes. If replicable on a large scale, this could provide short and long-term solution to LTC bed demand in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".