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Record W2334262609 · doi:10.1097/wad.0000000000000069

Comparison of an Electronic and Paper-based Montreal Cognitive Assessment Tool

2014· article· en· W2334262609 on OpenAlexaffabout
Anne Snowdon, Abdulkadir Hussein, Robert G. Kent de Grey, Lou Pino, Vladimir Hachinski

Bibliographic record

VenueAlzheimer Disease & Associated Disorders · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsWindsor Clinical ResearchUniversity of WindsorWestern University
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitionComputer sciencePsychologyCognitive impairmentNeuroscience

Abstract

fetched live from OpenAlex

This pilot study compared a novel electronic Montreal Cognitive Assessment (eMoCA) tool to the original paper-based MoCA. Potential participants were approached at primary care practices, a geriatric day hospital, and a university campus. Each of the 401 participants were randomly assigned to either the eMoCA (N=182) or MoCA (N=219). Scores were adjusted by self-reported demographic and health information using regression analysis. The difference in average scores (26.21±3.11 for the MoCA group and 24.84±4.21 for the eMoCA group) was found to be statistically significant. Controlling for the effect of potential covariate factors with regression analyses, the adjusted difference is -0.90 (95% confidence interval, -1.45 to -0.35). This difference may be due to factors related to use of the electronic device or software usability. However, the standardized, self-administered eMoCA may offer an opportunity for health systems to screen for early changes in cognitive function in primary care settings and offer greater access to assessment for rural or remote communities. Population-level research may be required to identify whether the score difference between test versions requires a downward adjustment to the eMoCA score taken as indicative of cognitive impairment.

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 score0.070
Threshold uncertainty score0.925

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.343
Teacher spread0.330 · 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

Citations35
Published2014
Admission routes2
Has abstractyes

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