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Record W4390079091 · doi:10.1017/s1355617723008627

3 Validity of the tele-administered Montreal Cognitive Assessment for identifying geriatric neurocognitive disorders

2023· article· en· W4390079091 on OpenAlexaboutno aff
Amtul-noor Rana, Bonnie M. Scott, Jared F. Benge, Robin C. Hilsabeck

Bibliographic record

VenueJournal of the International Neuropsychological Society · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentDementiaNeurocognitiveCognitionNeuropsychologyMedicineClinical psychologyNeuropsychological assessmentPsychologyPsychiatryCognitive impairmentDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Objective: With the emergence of the coronavirus 2019 pandemic, investigating the validity of tele-screenings for neuropsychological status has become increasingly necessary. While the telephone version of the Montreal Cognitive Assessment (MoCA-T) has been validated for use in patients with Parkinson’s and stroke/cerebrovascular disease, the clinical utility of this instrument in geriatric patients with other suspected cognitive disorders has yet to be determined. Thus, the present study aimed to examine the classification accuracy of the MoCA-T in a mixed clinical sample of patients with mild cognitive impairment (MCI) or dementia. Participants and Methods: Ninety-one older adults were administered the MoCA-T via videoconferencing technology as part of a comprehensive neurocognitive evaluation performed by a multidisciplinary treatment team within a dementia specialty clinic. Based on this evaluation, 51 (56.0%) patients were diagnosed with dementia, 27 (29.7%) with MCI, and 13 (14.3%) with no neurocognitive diagnosis (i.e., subjective cognitive complaints). In addition to MoCA-T total and item scores, we also computed subscale scores for between-group comparisons as the sum of items assessing orientation, language, attention/executive function, and memory. ANOVA/ANCOVA and ROC curve analyses were used to examine between-group differences on the MoCA-T and its psychometric properties in discriminating patients with MCI or dementia, respectively. Results: Participants had a mean age of 74.3 ± 8.7 and education of 16 ± 2.9 years. Patients with dementia were significantly older than those with MCI and no diagnosis, but there were no other significant between-group differences in clinical characteristics. MoCA-T total [F(2,86)=28.5, p<0.001] and all subscale scores (p<0.01) differed significantly between groups and in the expected direction (dementia Conclusions: The current study provides further evidence for the clinical utility of the MoCA-T as a screening instrument for neurocognitive disorders in older adults and extends prior work to include administration via videoconferencing technology. While previous studies have focused on the use of MoCA-T in specific patient populations, here, we demonstrate the validity of this screening tool in a mixed-clinical sample, which suggests its broader use in clinical settings for distinguishing between neurocognitive disorders, regardless of the underlying etiology.

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)0.016
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 score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.077
GPT teacher head0.414
Teacher spread0.336 · 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
Published2023
Admission routes1
Has abstractyes

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