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Record W7073682715

Predicting risk and outcomes for frail older adults:an umbrella review of frailty screening tools

2017· article· en· W7073682715 on OpenAlexaboutno aff

Bibliographic record

VenueAston Publications Explorer (Aston University) · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistData extractionSystematic reviewMEDLINEMeta-analysisRisk assessmentFrailty Index
DOInot available

Abstract

fetched live from OpenAlex

Background: A scoping search identified systematic reviews on diagnostic accuracy and predictive ability of frailty measures in older adults. In most cases, research was confined to specific assessment measures related to a specific clinical model. Objectives: To summarize the best available evidence from systematic reviews in relation to reliability, validity, diagnostic accuracy and predictive ability of frailty measures in older adults. Inclusion criteria population: Older adults aged 60 years or older recruited from community, primary care, long-term residential care and hospitals. Index test: Available frailty measures in older adults. Reference test: Cardiovascular Health Study phenotype model, the Canadian Study of Health and Aging cumulative deficit model, Comprehensive Geriatric Assessment or other reference tests. Diagnosis of interest: Frailty defined as an age-related state of decreased physiological reserves characterized by an increased risk of poor clinical outcomes. Types of studies: Quantitative systematic reviews. Search strategy: A three-step search strategy was utilized to find systematic reviews, available in English, published between January 2001 and October 2015. Methodological quality: Assessed by two independent reviewers using the Joanna Briggs Institute critical appraisal checklist for systematic reviews and research synthesis. Data extraction: Two independent reviewers extracted data using the standardized data extraction tool designed for umbrella reviews. Data synthesis: Data were only presented in a narrative form due to the heterogeneity of included reviews. Results: Five reviews with a total of 227,381 participants were included in this umbrella review. Two reviews focused on reliability, validity and diagnostic accuracy; two examined predictive ability for adverse health outcomes; and one investigated validity, diagnostic accuracy and predictive ability. In total, 26 questionnaires and brief assessments and eight frailty indicators were analyzed, most of which were applied to community-dwelling older people. The Frailty Index was examined in almost all these dimensions, with the exception of reliability, and its diagnostic and predictive characteristics were shown to be satisfactory. Gait speed showed high sensitivity, but only moderate specificity, and excellent predictive ability for future disability in activities of daily living. The Tilburg Frailty Indicator was shown to be a reliable and valid measure for frailty screening, but its diagnostic accuracy was not evaluated. Screening Letter, Timed-up-and-go test and PRISMA 7 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) demonstrated high sensitivity and moderate specificity for identifying frailty. In general, low physical activity, variously measured, was one of the most powerful predictors of future decline in activities of daily living. Conclusion: Only a few frailty measures seem to be demonstrably valid, reliable and diagnostically accurate, and have good predictive ability. Among them, the Frailty Index and gait speed emerged as the most useful in routine care and community settings. However, none of the included systematic reviews provided responses that met all of our research questions on their own and there is a need for studies that could fill this gap, covering all these issues within the same study. Nevertheless, it was clear that no suitable tool for assessing frailty appropriately in emergency departments was identified.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.104
GPT teacher head0.262
Teacher spread0.158 · 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 designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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