Do Geriatric Interventions Reduce Emergency Department Visits? A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Hospital emergency departments (EDs) serve an aging population with an increased burden on health resources. Few studies have examined the effects of comprehensive geriatric assessment interventions on ED use. This study aimed to systematically review the literature and compare the effects of these interventions on ED visits. METHODS: Relevant articles were identified through electronic databases and a search of reference lists and personal files. Inclusion criteria included: original research (written in English or French) on interventions conducted in noninstitutionalized populations 60 years old or older, not restricted to a particular medical condition, in which ED visits were a study outcome. Data were abstracted and checked by the first author and a research assistant using a standard protocol. RESULTS: Twenty-six relevant studies were identified, reported in 28 articles, with study samples obtained from EDs (9), hospitals (4), outpatient or primary care settings (10), home care (4), and community (1). The study designs included 17 randomized controlled trials, 3 trials with nonrandom allocation, 4 before-after studies, 1 quasi-experimental time-series study, and 1 cross-sectional study. Hospital-based interventions (mostly short-term assessment and/or liaison) had little overall effect on ED utilization, whereas many interventions in outpatient and/or primary care or home care settings (including geriatric assessment and management and case management) reduced ED utilization. Heterogeneity in study methods, measures of comorbidity, functional status, and ED utilization precluded meta-analysis of the results. CONCLUSION: Further research, using improved methodologies and standardized measures, is needed to address the effects of innovative geriatric interventions on ED visits.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".