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Record W2964837038 · doi:10.1016/j.jamda.2019.06.015

A 4-Item Case-Finding Tool to Detect Dementia in Older Persons

2019· article· en· W2964837038 on OpenAlexaboutno aff
Tau Ming Liew

Bibliographic record

VenueJournal of the American Medical Directors Association · 2019
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Medical Research CouncilMedical Research CouncilNational Institute on AgingNational Institutes of Health
KeywordsMontreal Cognitive AssessmentDementiaMedicineReceiver operating characteristicRecallCognitionMemory clinicCognitive impairmentTest (biology)GerontologyDiseasePsychiatryInternal medicineCognitive psychologyPsychology

Abstract

fetched live from OpenAlex

OBJECTIVES: Brief cognitive tests are recommended in clinical services outside of specialized memory clinics as case-finding tools to reduce the diagnostic gap of dementia. Although the Montreal Cognitive Assessment (MoCA) is among the most widely used brief tests in specialized memory clinics, its length precludes routine use in nonspecialty clinics. This study investigated whether a small subset of MoCA would suffice to match the performance of the full MoCA in detecting dementia and, hence, be useful in nonspecialty clinics. DESIGN: Cross-sectional test research. SETTING: Alzheimer's Disease Centers across the United States. PARTICIPANTS: Participants age ≥65 years (n = 8773). MEASURES: Participants completed MoCA and were evaluated for dementia. The study sample was split into 2: the derivation sample (n = 4386) was used to develop a short variant of MoCA that best distinguish dementia (using the best-subset-approach with 10-fold cross-validation), while the validation sample (n = 4387) verified its actual performance using area under the receiver operating characteristic-curve (AUC). RESULTS: A 4-item cognitive test was identified, comprising Clock-drawing, Tap-at-letter-A, Orientation, and Delayed-recall. It demonstrated excellent performance in distinguishing dementia from nondementia (AUC 94.2%) and was comparable to that of MoCA (AUC 93.8%), even across education subgroups. It explained 85.9% of the variability in MoCA and had scores that could be mapped to MoCA with reasonable precision. At the optimal cut-off score of <10, it demonstrated 87.9% sensitivity and 87.6% specificity in detecting dementia. CONCLUSIONS AND IMPLICATIONS: Using rigorous methods, this study developed a brief cognitive test that is free of charge, takes <5 minutes to complete, covers the key cognitive domains, and has standardized instructions to allow its administration even by nonphysicians. This brief test is well suited as a case-finding tool in nonspecialty clinics (such as in primary care and geriatric clinics) and may improve care-integration with specialized memory clinics that utilize MoCA.

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)0.005
metaresearch head score (Gemma)0.020
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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.315
Teacher spread0.306 · 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".

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Citations17
Published2019
Admission routes1
Has abstractno

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