Predictive Strategies to Reduce the Risk of Rehospitalization with a Focus on Frail Older Adults: A Narrative Review
Bibliographic record
Abstract
Frailty is a geriatric syndrome that has physical, cognitive, psychological, social, and environmental components and is characterized by a decrease in physiological reserves. Frailty is associated with several adverse health outcomes such as an increase in rehospitalization rates, falls, delirium, incontinence, dependency on daily living activities, morbidity, and mortality. Older adults may become frailer with each hospitalization; thus, it is beneficial to develop and implement preventive strategies. The present review aims to highlight the epidemiological importance of frailty in rehospitalization and to compile predictive strategies and related interventions to prevent hospitalizations. Firstly, it is important to identify pre-frail and frail older adults using an instrument with high validity and reliability, which can be a practically applicable screening tool. Comprehensive geriatric assessment-based care is an important strategy known to reduce morbidity, mortality, and rehospitalization in older adults and aims to meet the needs of frail patients with a multidisciplinary approach and intervention that includes physiological, psychological, and social domains. Moreover, effective multimorbidity management, physical activity, nutritional support, preventing cognitive frailty, avoiding polypharmacy and anticholinergic drug burden, immunization, social support, and reducing the caregiver burden are other recommended predictive strategies to prevent post-discharge rehospitalization in frail 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".