A Reliable Tool for Assessing MCI and Dementia: Validation Study of DemTect for Turkish Population
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".