KAZAKH ADAPTATION OF THE MONTREAL COGNITIVE ASSESSMENT (MOCA).
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is a brief cognitive evaluation tool that has been developed for screening of patients for Mild Cognitive Impairment (MCI). MCI is a recognized high-risk state for Alzheimer's disease development. The aim of the present study was to create a Kazakh-language adaptation of the original version of the Montreal Cognitive Assessment (version 7.1) and evaluate its reliability by determining internal consistency using the Cronbach's alpha coefficient. This prospective study involved 50 patients diagnosed with Parkinson's disease in accordance with the 2015 MDS clinical criteria with diagnosed MCI according to clinical guidelines of the Movement disorder society (MDS). Clinical and neuropsychological evaluation were carried out on all patients. The internal consistency and reliability of the translated scale were investigated by means of the Cronbach alpha coefficient. The Cronbach's alpha coefficient for the MoCA Kazakh version was 0.77. While the evaluation of discriminatory validity was not performed in this study, the Kazakh adaptation of the MoCA was shown to be a reliable tool for screening MCI among patients with Parkinson's Disease.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".