Frailty assessment in emergency medicine using the Clinical Frailty Scale: a scoping review
Bibliographic record
Abstract
BACKGROUND: Frailty is a common condition present in older Emergency Department (ED) patients that is associated with poor health outcomes. The Clinical Frailty Scale (CFS) is a tool that measures frailty on a scale from 1 (very fit) to 9 (terminally ill). The goal of this scoping review was to describe current use of the CFS in emergency medicine and to identify gaps in research. METHODS: We performed a systemic literature search to identify original research that used the CFS in emergency medicine. Several databases were searched from January 2005 to July 2021. Two independent reviewers completed screening, full text review and data abstraction, with a focus on study characteristics, CFS assessment (evaluators, timing and purpose), study outcomes and statistical methods. RESULTS: A total of 4818 unique citations were identified; 34 studies were included in the final analysis. Among them, 76% were published after 2018, mainly in Europe or North America (79%). Only two assessed CFS in the pre-hospital setting. The nine-point scale was used in 74% of the studies, and patient consent was required in 69% of them. The main reason to use CFS was as a main exposure (44%), a potential predictor (15%) or an outcome (15%). The most frequently studied outcomes were mortality and hospital admission. CONCLUSION: The use of CFS in emergency medicine research is drastically increasing. However, the reporting is not optimal and should be more standardized. Studies evaluating the impact of frailty assessment in the ED are needed. REGISTRATION: https://doi.org/10.17605/OSF.IO/W2F8N.
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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 | 0.017 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".