P4‐327: Montreal Cognitive Assessment (MoCA) population‐based study of Russian elderly
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is a widely used cognitive screening tool for Mild Cognitive Impairment (MCI) and early Alzheimer's disease (AD). However, there are no published population-based studies to guide its use with Russian populations. The aim of the current study was to investigate the impact of demographic variables on MoCA total score, and to describe its use as a screening tool for AD prevention studies in an urban population of older adults from Tomsk, Russia. Volunteers (N=1377) aged 60–89 years were identified using a centralized medical care system database. Subjects were asked by phone to participate in a pre-screening study for a future primary prevention clinical trial. Participants were administered the Russian translation of the MoCA, and specifically asked if they would consider participating in a pharmacological trial for the prevention of AD. No significant differences in MoCA total score were found between men (N=350) and women (N=1027). Lower education was associated with poorer performance on MoCA total scores. Those with less than high school education had the lowest total MoCA scores (17.8±0.3), followed by high school graduates (21.7±0.3), those with some college (21.5±0.2), college graduates (22.9±0.1), and those with a graduate degree (23.7±0.4), p<0.0001. Older age was associated with poorer performance on MoCA total scores. Individuals aged 83–89 years (n=38) had the lowest MoCA total scores (19.2±0.6), followed by ages 79–82, 19.7±0.4 (n=90), ages 73–78, 20.8±0.2 (n=383), and ages 67–72, 22.01±0.2 (n=501); ages 60–66 performed best, 23.3±0.2 (n=365), p<0.0001. Most participants (93%) expressed interest in participating in a pharmacological, primary prevention clinical trial. There were no differences in interest based on gender or total MoCA score. However, individuals in the oldest age band were less likely to express interest (95% of 60–66 year-olds and 82% of 83–89 year-olds, p<0.0001). The MoCA was a useful screening tool in a Russian elderly population, though education and age should be considered when using the MoCA to screen for clinical trials. The methods used in this study can be effective to recruit older Russian adults for pharmacological prevention trials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".