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Record W2732521767 · doi:10.1093/geroni/igx004.3651

DETECTION OF COGNITIVE FRAILTY WITH THE COMPREHENSIVE FRAILTY ASSESSMENT INSTRUMENT

2017· article· en· W2732521767 on OpenAlexaboutno aff
E.E. DeRoeck, Nico De Witte, Sarah Dury, Maria Bjerke, PP De Deyn, S. Engelborghs, Eva Dierckx

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldEngineering
TopicEngineering Diagnostics and Reliability
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaMontreal Cognitive AssessmentCognitionGerontologyPsychologyReliability (semiconductor)Multivariate analysisCognitive declineEconomic shortageCognitive Assessment SystemClinical psychologyCognitive impairmentMedicinePsychometricsPsychiatryDementiaDisease

Abstract

fetched live from OpenAlex

The Comprehensive Frailty Assessment Instrument (CFAI) measures four domains of frailty: physical, psychological, social and environmental frailty. The absence of cognitive frailty can be seen as a shortage. Therefore we conducted a study in which we administered to 355 older adults (mean age 77.62) the CFAI, The Montreal Cognitive Assessment (MoCA) and 6 questions about cognition that were chosen by an expert panel and based on the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). Based on multivariate analysis with SIMCA software and Receiver operating curves two uninformative questions were excluded. The sum of the four remaining questions forms the new cognitive frailty domain. This domain shows good concurrent validity with the MoCA and with the added questions the reliability of the CFAI remains good (Cronbach’s alpha: .789). So in sum, cognitive frailty can be added to the CFAI.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.026
GPT teacher head0.284
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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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