Cognitive disorders in the elderly persons and their psychometric markers
Bibliographic record
Abstract
Introduction Population aging is accompanied by an increase in mental disorders of late age, cognitive impairment and dementia, which makes research on their diagnosis, prevention and therapy particularly relevant. Objectives In order to improve the diagnosis of cognitive disorders in elderly patients, a comparative characteristic of the MMSE and MoCA psychometric scales is presented. Due to the lack of differentiation of results in the MoCA - test according to the severity of cognitive disorders, ranking was carried out to determine the scoring levels of cognitive decline. Methods On the clinical basis of the scientific department of gerontopsychiatry of the Moscow Research Institute of Psychiatry - branch of the V. Serbsky National Medical Research Center for Psychiatry and Narcology, 46 people over 60 years old were examined. The identified mental disorders were coded under the ICD-10 F00-F09 rubric “Organic, including symptomatic mental disorders”. Cognitive status assessment was carried out by psychometric scales MMSE and MoCA. Psychometric and statistical research methods were used. To compare MoCA and MMSE scores, an equal-percentage alignment method was used Results In patients examined by MMSE and MoCA, different point values were determined in assessing cognitive functions. In most observations, the MoCA- test indicators were lower than the MMSE values. The following correspondences of the score values of the scales were revealed: MOCA 23-30 - MMSE 28-30 points; MOCA 22-18 - MMSE 27-25; MOCA 17-12 – MMSE 23-20; MOCA 12-0 – MMSE 19-0. Conclusions Such features of the MMSE scale as insufficient sensitivity in assessing memory impairment and differentiation of non-dementia levels of cognitive impairment, regulatory functions of programming and goal-setting, lexical fluency were revealed. The advantages of the MoCA - test were the ability to assess visual-constructive and executive skills, praxis, a more accurate assessment of memory impairments, mobility of mental processes and the ability to switch, logical thinking, the possibility of topical diagnosis of brain damage. The disadvantages of the MoCA - test were the duration of its implementation, the fatigue of patients, the lack of tasks for assessing written speech and motor praxis. The MoCA - test is a more sensitive method for detecting and differentiating cognitive impairment compared to the MMSE scale, which makes it possible to recommend it for widespread introduction into psychiatric practice. Relevant in further studies are the determination of indicators of moderate cognitive impairment as a threshold value between mild cognitive impairment (MCI) and incipient dementia, clarification of the point values of different levels of dementia. Disclosure of Interest None Declared
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".