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Record W3088619072 · doi:10.1177/1533317520949805

A Reliable Tool for Assessing MCI and Dementia: Validation Study of DemTect for Turkish Population

2020· article· en· W3088619072 on OpenAlexaboutno aff
Gözde Şengül Ayçiçek, Hatice Çalışkan, Cemile Özsürekçi, Pelin Ünsal, Josef Kessler, Elke Kalbe, Mert Eşme, Rana Tuna Doğrul, Cafer Balcı, Ümran Sema Seven, Erdem Karabulut, Meltem Halil, Mustafa Cankurtaran, Burcu Balam Doğu

Bibliographic record

VenueAmerican Journal of Alzheimer s Disease & Other Dementias® · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaMontreal Cognitive AssessmentInter-rater reliabilityMedicineCognitive impairmentTurkishPopulationMini–Mental State ExaminationCognitionMemory clinicPsychologyAudiologyPhysical therapyInternal medicinePsychiatryDevelopmental psychologyRating scaleDisease

Abstract

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BACKGROUND AND AIM: Mild cognitive impairment (MCI) and dementia prevalence are expected to increase with aging. The DemTect is a very quick and easy tool to administer and recognize the early stages of dementia and MCI. In this study we aimed to evaluate the reliability and validity of a Turkish version of the DemTect and define cut off values for different age and educational levels. One of our aims is also to compare the sensitivity and specifity of the DemTect to other common screening tools. PATIENTS AND METHODS: Fifty-four patients with MCI, 55 patients with dementia and 91 patients with subjective memory complaints (SMC) were enrolled in the study. The DemTect was translated into Turkish by forward-backward translation and compared with the Mini Mental State Examination (MMSE), the Quick Mild Cognitive Impairment Turkish version (QMCI-TR) and the Montreal Cognitive Assessment (MoCA). In order to test interrater reliability, the DemTect was administered to 11 patients, on the same day, by 2 trained raters. To establish test-retest reliability, the same rater scored the tool a second time on 11 patients within 2 weeks. RESULTS: The median age of the patients was 73 (min-max: 65-90) years, 54.5% were female. We found a strong correlation between DemTect scores and the MMSE, the QMCI, and the MoCA (r = 0.725, r = 0.816, r = 0.821, respectively; p < 0.001). In ROC analysis, the cut-off point of the DemTect to differentiate MCI from SMC was 11.5 with 92.6% sensitivity, 91.2% specificity, AUC 0.973 and the cut-off point of the DemTect to differentiate dementia from SMC was 9.5 with 96.4% sensitivity, 100% specificity, AUC 0.916. Cronbach α was 0.823. Intraclass correlation coefficient was 0.873 (95% CI: 0.598-0.964) for interrater reliability and 0.966 (95% 0.777-0.982) for test-retest reliability (Cronbach α = 0.932, 0.966 respectively). CONCLUSION: The DemTect is a very reliable tool to assess Turkish patients with MCI and dementia.

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)0.006
metaresearch head score (Gemma)0.011
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 score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.038
GPT teacher head0.353
Teacher spread0.315 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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