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Record W2783843549 · doi:10.1097/wad.0000000000000240

The MoCA-Memory Index Score

2018· article· en· W2783843549 on OpenAlexaboutno aff
Antarpreet Kaur, Steven D. Edland, Guerry M. Peavy

Bibliographic record

VenueAlzheimer Disease & Associated Disorders · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on AgingNational Institutes of HealthUniversity of Washington
KeywordsMontreal Cognitive AssessmentRecallReceiver operating characteristicAudiologyParagraphMedicineLogistic regressionPsychologyCognitive impairmentCognitionCognitive psychologyInternal medicinePsychiatryComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare ability of 2 measures of delayed memory (word list, story paragraph) to discriminate Normal Control (NC) subjects from those with amnestic mild cognitive impairment (aMCI). METHODS: Demographic, neuropsychological, and diagnostic data contributed by 34 Alzheimer's Disease Centers to the National Alzheimer's Coordinating Center characterized 2717 individuals with a diagnosis of either NC (n=2205) or aMCI (n=512). The Montreal Cognitive Assessment-Memory Index Score (MoCA-MIS) assessed delayed word recall, and the Craft Story 21, delayed story recall. Logistic regression and receiver operator characteristic curves controlling for age, sex, and education assessed the ability of each test to differentiate NCs from subjects with aMCI. RESULTS: The MoCA-MIS had significantly better sensitivity and specificity (area under the receiver operator characteristic curve 0.83 vs. 0.80, P=0.004). At sensitivity 80%, the specificity of the MoCA-MIS was 69.1%, compared with 62.8% for the Craft Story. CONCLUSIONS: These data suggest that the MoCA-MIS, a recall score from items within the MoCA, is better at discriminating NCs from subjects with aMCI than the Craft Story. Word recall may be an efficient alternative to paragraph recall for diagnostic screening within clinical practice and research 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.128
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.300
Teacher spread0.282 · 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

Citations44
Published2018
Admission routes1
Has abstractyes

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