Frailty assessment utilization around the globe–a systematic review
Bibliographic record
Abstract
BACKGROUND: Recent expert guidelines recommend that frailty assessments (FAs) encompass physical, functional, cognitive, social, and mental health domains. This systematic review examines FAs administered globally between 2015 to 2022 in geriatric participants (65 years and older) to characterize the parameters used to assess frailty. METHODS: Following PRISMA guidelines, we screened 3,859 articles and included 202 in the final analysis. FA parameters were coded into 45 health-related categories defined by the authors to evaluate the domains most frequently used. RESULTS: Across 39 countries, 291 FAs were identified, with an average number of 17.36 parameters per instrument. Of the 4,995 total parameters analyzed, 22.32 % assessed functional health or physical performance. Cognitive, mental, and social health were assessed by only 6.09 %, 6.35 %, and 5.01 % of parameters, respectively. CONCLUSIONS: FAs overwhelmingly measure functional and physical health parameters with limited attention to cognitive, mental, and social domains. This imbalance suggests that instruments may fall short of capturing the multidimensional nature of frailty as recommended by recent guidelines. By cataloging current FAs, their components, and the degree to which they reflect comprehensive frailty definitions, this review highlights the need for further research and refinement of FAs to ensure accurate, holistic assessment across diverse clinical settings.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".