Predicting Adverse Outcomes After Discharge From Complex Continuing Care Hospital Settings to the Community
Bibliographic record
Abstract
PURPOSE OF STUDY: The purpose was to identify risk and protective factors assessed at complex continuing care (CCC) admission that were associated with three adverse outcomes (death, readmission, and incidence of or failure to improve possible depression) for persons discharged from CCC to the community with home care services. PRIMARY PRACTICE SETTINGS: CCC, home care, community. METHODOLOGY AND SAMPLE: The sample included all CCC patients in Ontario assessed with the Resident Assessment Instrument-Minimum Data Set 2.0 between January 2003 and December 2010 and who were subsequently assessed with the Resident Assessment Instrument-Home Care within 6 months of discharge to the community (n = 9,940). Separate multivariable logistic regression models were developed for each outcome. RESULTS: Within 6 months, 4.9% of the sample had died, 6.5% were readmitted to any Ontario CCC facility, and 13.7% showed symptoms of new possible depression or failure to improve possible depression. Heart failure, chronic obstructive pulmonary disease (COPD), health instability, intravenous/tube feed, and pressure ulcer were associated with increased risk of death. Difficulty with comprehension, possible depression, COPD, unstable conditions, acute episode or flare-up, short-term prognosis, worsening self-sufficiency, and having either patient or caregiver optimistic about discharge were associated with increased risk of readmission. Existing depressive symptoms or depression, unsettled relationships, multimorbidity, and polypharmacy were associated with risk for incidence of or failure to improve possible depression. Optimism about rehabilitation potential and high social engagement were protective against readmission and depressive outcomes, respectively. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: Person-level clinical data collected on admission to CCC can be used to identify high-risk patients and trigger early discharge planning processes and other in-home interventions. These results support the sharing of information between settings, and highlight key areas in which care teams in CCC and case managers in home care organizations can work together to support the transition to home and potentially reduce adverse postdischarge outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".