Patient outcomes related to receiving care on a dedicated Acute Care for Elders ( <scp>ACE</scp> ) unit versus with an <scp>ACE</scp> order set
Bibliographic record
Abstract
BACKGROUND: The Acute Care for Elders (ACE) unit model of care aims to reduce common complications of hospitalization in older adults through early involvement of allied health providers, changes to the care environment, elder-friendly care protocols, and proactive discharge planning. Our hospital established a dedicated 28-bed medical ACE unit. Because of capacity limitations, the number of eligible older medical patients often exceeds the available number of beds. Thus, some ACE unit-eligible patients are instead admitted to other medical or surgical units for their medical care. These "bed-spaced" ACE patients receive care by the same general internists and ACE order set that ACE unit patients are cared under. We sought to compare the health outcomes of ACE-designated patients admitted to the ACE unit versus bed-spaced peers cared for using a protocolized ACE order set. METHODS: 3046 ACE-designated patient admissions were analyzed (1499 ACE unit and 1547 bed-spaced). The primary outcomes examined were discharge disposition and in-hospital mortality. Univariate and multivariate comparisons were performed. Propensity matching was used to adjust for case mix in a post-hoc analysis. RESULTS: The mean age of participants was 83.5 years for ACE unit patients and 82.6 for bedspaced patients. In adjusted models, ACE unit patients were more likely to be discharged home (OR 1.28 [1.08-1.50], p = 0.003). In an unadjusted analysis, patients admitted to ACE unit were less likely to die in hospital, but this finding did not persist after adjustment for case mix. CONCLUSION: Care of older adults delivered on a dedicated ACE unit increases the likelihood of discharge to home when compared to care delivered with an ACE order set alone for general internal medicine patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".