Acute Care for Elders Components of Acute Geriatric Unit Care: Systematic Descriptive Review
Bibliographic record
Abstract
OBJECTIVES: To describe the Acute Care for Elders (ACE) model components implemented as part of acute geriatric unit care and explore the association between each ACE component and outcomes of iatrogenic complications, functional decline, length of hospital stay, nursing home discharges, costs, and discharges home. DESIGN: Systematic descriptive review of 32 articles, including 14 trials reporting on the implementation of ACE components or the effectiveness of their implementation in improving outcomes. Mean effect sizes (ESs) were calculated using trial outcome data. Information describing implementation of the ACE components in the trials was analyzed using content analysis. SETTING: Acute care geriatric units. PARTICIPANTS: Acutely ill or injured adults (N = 6,839) with an average age of 81. INTERVENTIONS: Acute geriatric unit care was characterized by the implementation of one or more ACE components: medical review, early rehabilitation, early discharge planning, prepared environment, patient-centered care. MEASUREMENTS: Falls, pressure ulcers, delirium, functional decline, length of hospital stay, discharge destination (home or nursing home), and costs. RESULTS: Medical review, early rehabilitation, and patient-centered care, characterized by the implementation of standardized and individualized function-focused interventions, had larger standardized mean ESs (all ES = 0.20) averaged across all outcomes, than did early discharge planning (ES = 0.17) or prepared environment (ES = 0.11). CONCLUSION: Specific ACE component interventions of medical review, early rehabilitation, and patient-centered care appear to be optimal for overall positive outcomes. These findings can help service providers design and evaluate the most-effective ACE model within the contexts of their respective institutions to improve outcomes for acutely ill or injured older adults.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".