MétaCan
Menu
Back to cohort
Record W2738458940 · doi:10.1159/000478008

Assessing Capacity in the Elderly: Comparing the MoCA with a Novel Computerized Battery of Executive Function

2017· article· en· W2738458940 on OpenAlexaffabout
Megan Brenkel, Kenneth I. Shulman, Elias Hazan, Nathan Herrmann, Adrian M. Owen

Bibliographic record

VenueDementia and Geriatric Cognitive Disorders Extra · 2017
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsWestern UniversitySunnybrook Hospital
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitionExecutive functionsPsychologyGerontologyMedicineCognitive impairmentPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: Clinicians are increasingly being asked to provide their opinion on the decision-making capacity of older adults, while validated and widely available tools are lacking. We sought to identify an online cognitive screening tool for assessing mental capacity through the measurement of executive function. METHODS: A mixed elderly sample of 45 individuals, aged 65 years and older, were screened with the Montreal Cognitive Assessment (MoCA) and the modified Cambridge Brain Sciences Battery. RESULTS: Two computerized tests from the Cambridge Brain Sciences Battery were shown to provide information over and above that obtained with a standard cognitive screening tool, correctly sorting the majority of individuals with borderline MoCA scores. CONCLUSIONS: The brief computerized battery should be used in conjunction with standard tests such as the MoCA in order to differentiate cognitively intact from cognitively impaired older adults.

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)0.008
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.073
GPT teacher head0.343
Teacher spread0.270 · 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

Citations38
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueDementia and Geriatric Cognitive Disorders ExtraSame topicHealthcare Decision-Making and RestraintsFrench-language works237,207