Evidence-based Risk Factors for Adverse Health Outcomes in Older Patients after Discharge Home and Assessment Tools: A Systematic Review
Bibliographic record
Abstract
The current health care system is discharging elderly patients "quicker" and "sicker" from acute care facilities. Consequently, hospital readmission is common; however, readmission may be only one aspect of adverse outcomes of importance to social work discharge planners. The early recognition of risk factors might ensure a successful transition from the hospital to the home. A systematic review was conducted to identify factors associated with adverse outcomes in older patients discharged from hospital to home. Using a content analysis, factors were characterized in five domains: demographic factors, patient characteristics, medical and biological factors, social factors, and discharge factors. The most frequently reported risks were depression, poor cognition, comorbidities, length of hospital stay, prior hospital admission, functional status, patient age, multiple medications, and lack of social support. A systematic search identified four discharge assessment tools for use with the general population of elderly patients. Practice and research implications are offered.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".