Bibliographic record
Abstract
Abstract Purpose of the Review Globally, emergency departments are recognizing their rapidly growing number of older adult patients and some have responded with care models and associated processes broadly described under the umbrella of geriatric emergency departments (Geriatric EDs). This review seeks to identify emerging themes in the Geriatric ED literature from the period 2018–2023 to provide a synthesis of concepts and research to assist emergency medicine healthcare professionals and policymakers in improving the delivery of emergency medical care to older patients. Recent Findings Emerging themes in Geriatric EDs include “calls to action” in the field regarding 1) health system level integration; 2) developing care processes; 3) implementing minimum Geriatric ED standards; and, 4) setting future research agendas. The research is international in scope with contributions from Canada, Australia, United Kingdom, Belgium, and the United States among others. A focus on Geriatric EDs’ financial sustainability as well as the overall efficacy of the care model is apparent. Recent seminal resources in Geriatric EDs include the Geriatric Emergency Department Collaborative, the Geriatric Emergency Care Applied Research Network, and the Geriatric Emergency Department Accreditation program. Attention to workforce education and specific care process/protocols for screening/assessment, cognitive dysfunction and falls is growing. Overall findings support the effectiveness and potential of Geriatric EDs in enhancing emergency care for older adults. Summary A review providing an overview of current themes and future directions of Geriatric EDs through a thematic analysis of the current literature. Key Geriatric ED themes include four “calls for action”, assessment of the model’s financial sustainability, an examination of the model’s efficacy and quality, and an identification of key resources foundational to Geriatric EDs. Targeted Geriatric ED workforce education programs and attention to care processes are contributing to improving outcomes for older adult in the ED.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".