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Record W2811267243 · doi:10.1155/2018/3280621

Cognitive Assessment Test: Validation of a Short Cognitive Test for the Detection of Mild Cognitive Disorder

2018· article· en· W2811267243 on OpenAlexaboutno aff
Kelly Estrada-Orozco, Kely Bonilla-Vargas, Francy Cruz‐Sanabria, Oscar Mancera, Miguel Ángel Ruiz, Laura María Núñez Álvarez, Rodrigo Pardo, Humberto Arboleda

Bibliographic record

VenueInternational Journal of Alzheimer s Disease · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersUniversidad Nacional de Colombia
KeywordsCognitionTest (biology)MedicineCognitive testClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Cognitive disorders are a clinical and research challenge; in particular, the mild cognitive disorder (MiCD) requires diagnostic suspicion and tools with adequate performance for its detection. The objective of this study was the validation of a short cognitive test (CATest) for the detection of MiCD in population of 50 years or more. METHODS: A diagnostic accuracy study was assembled and performed in a prospective cohort. A consecutive sample of 200 Colombian subjects who represented the whole spectrum of the condition of interest allowed us to reach the objective. Validity was determined by concurrent criteria. The cut points were determined by the ROC curves considering the best overall performance and accuracy of the test. RESULTS: 95% 61.8 to 79.43), positive predictive value of 75% ( 95% CI 66.79 to 82.42), and area under curve AUC 0.8518 (standard error SE 0.0265). DISCUSSION: CATest has an adequate performance as a short cognitive test for the detection of MiCD. Its performance is superior to MiniMental and similar to Montreal Cognitive test (MoCA) according to the data reported in the literature. The advantages over other tests are the evaluation of all cognitive domains, time of application, and easy interpretation of results. CATest is a free use alternative for MiCD detection.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.161
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.040
GPT teacher head0.400
Teacher spread0.360 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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