Frailty in Older Adults Using Pre-hospital Care and the Emergency Department: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: Older adults use more health-care services per capita than younger age groups and the older adult population varies greatly in its needs. Evidence suggests that there is a critical distinction between relative frailty and fitness in older adults. Here, we review how frailty is described in the pre-hospital literature and in the broader emergency medicine literature. METHODS: PubMed was used as the primary database, but was augmented by searches of CINAHL and EMBASE. Articles were included if they focused on patients 60 years and older and implemented a definition of frailty or risk screening tool in the Emergency Medical Services (EMS) or Emergency Department setting. RESULTS: IN THE BROAD CLINICAL LITERATURE, THREE TYPES OF MEASURES CAN BE IDENTIFIED: frailty index measures, frailty scales, and a phenotypic definition. Each offers advantages and disadvantages for the EMS stakeholder. We identified no EMS literature on frailty conceptualization or management, although some risk measures from emergency medicine use terms that overlap with the frailty literature. CONCLUSIONS: There is a paucity of research on frailty in the Emergency Medical Services literature. No research was identified that specifically addressed frailty conceptualization or management in EMS patients. There is a compelling need for further research in this area.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.003 | 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".