Effectiveness of an assess and restore program in treating older adults with physiological and functional decline: The HEART program
Bibliographic record
Abstract
OBJECTIVES: The study aimed to determine the effectiveness of an "assess and restore" model, Humber's Elderly Assess and Restore Team (HEART) program, in reducing length of stay, avoiding becoming designated as alternate level of care (ALC), facilitating home discharge, and reducing hospital readmissions. METHODS: The electronic health records of community-dwelling adults aged ≥65 years admitted to a large community hospital from September 4, 2018 to March 31, 2020 were extracted. Propensity score matching was used to compare HEART participants and patients eligible for the program who did not participate in terms of excessive length of stay, ALC status, discharge destination, 30-day hospital readmission, and 30-day visits to the emergency department. Mann-Whitney U tests and regression analyses were used to determine associations between HEART participation and outcome variables. RESULTS: After propensity score matching, 1094 patients were included: 547 HEART participants and 547 non-participants. Compared to non-participants, HEART patients had a lower excessive length of stay (Mdn=0.1 vs 0.5 days, p=.04), were less likely to become ALC (OR=0.30, 95% CI=0.13-0.69), and were more likely to be discharged home (OR=2.85, 95% CI=2.03-3.99). HEART participation was not associated with 30-day readmission to the hospital nor emergency department visits. CONCLUSIONS: The HEART program can preserve hospital resources and reduce the need for further rehabilitative care but does not affect future visits to the hospital. An assess and restore program may be beneficial in the care of hospitalized older adults.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".