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Record W4414805275 · doi:10.1016/j.tjfa.2025.100088

Frailty assessment utilization around the globe–a systematic review

2025· article· en· W4414805275 on OpenAlexfundno aff
Samantha Gaston, Elle Billman, Lichy Han, David R. Drover

Bibliographic record

VenueThe Journal of Frailty & Aging · 2025
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
FundersInstitute of AgingNational Institutes of Health
KeywordsHealthy agingMEDLINEOlder peopleFrailty IndexMeasure (data warehouse)Physical activity

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.380
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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