The psychometric assessment results of the Russian version of the Cognitive test for severe dementia
Bibliographic record
Abstract
Objectives. To adapt the Russian version of the Cognitive Test for Severe Dementia (CTSD–Rus) using psychometric indicators (reliability, validity). Material and methods. The CTSD scale was translated directly and back-translated with cultural adaptation and approval from the author of the original CTSD. The study involved 118 patients (mean age 66±11.2 years) with dementia of various etiologies and severity living in a nursing home. Cognitive status was assessed using neuropsychological scales: CTSD-Rus, MMSE, MoCA, CDR. To assess reliability (inter-expert and test-retest), subgroups of patients (N=54 and N=52, respectively) with CDR≥3 and MMSE<10 were selected. The following coefficients were calculated: Cronbach’s alpha, intraclass correlation coefficient (ICC), Spearman’s rank correlation coefficient. The study was approved by the biomedical ethics committee of the Republican Scientific and Practical Center for Medical Examination and Rehabilitation (No. 1/2 dated January 7, 2022). Results. CTSD-Rus showed high internal consistency (Cronbach’s alpha = 0.94). Inter-rater and test-retest reliability for most items and the total score was excellent (ICC>0.75; r>0.9). The high validity of CTSD-Rus was established, which is confirmed by a significant correlation with the MMSE and MoCA scales (r=0.893 and r=0.856, respectively) for the entire sample and the sample with severe dementia at CDR≥3 (r=0.758 and r=0.681, respectively). With zero MMSE and MoCA values, the use of CTSD-Rus shows a spread of scores (5.9±7.1 and 8.83±6.3, respectively), which allows for a more in-depth assessment of preserved cognitive functions. Conclusions. The study showed that CTSD-Rus is primarily focused on patients with severe and more than severe dementia and can be used by researchers and healthcare professionals for a detailed assessment of preserved cognitive functions in this category of patients.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".