Mobile Early Detection Memory Screening in the Republic of Armenia
Bibliographic record
Abstract
Abstract Background The Republic of Armenia is a post‐Soviet, low‐ and middle‐income country (LMIC) in the south Caucasus region with a steadily increasing aging population. The goal of this study was to provide the first look into the national cognitive health in Armenia, considering the growing burden of cognitive impairment (CI) and widespread lack of public awareness about dementia. As a component of the early detection memory screening program launched by Alzheimer’s Care Armenia’s Brain Health Project and funded through Davos Alzheimer’s Collaborative (DAC), this study aimed to understand the prevalence of CI and associated factors across the adult population. Methods Utilizing a mobile clinic, a sample of 4,066 adults (aged 25‐94) were screened for cognitive impairment across 8 urban and rural provinces in Armenia. Participants completed a Montreal Cognitive Assessment (MoCA) screening test and Health Characteristic Questionnaire including items about health behaviors and chronic health conditions. Statistical analyses were used to investigate demographic trends of CI and test for significant associations. Results MoCA scores indicated the following cognitive levels in this population: 71.2% normal cognition, 23.7% mild cognitive impairment, 4.2% moderate cognitive impairment, and 0.8% severe cognitive impairment. The most prevalent chronic conditions included history of COVID, hypertension, history of depression, and history of heart disease (Table 1). The most common health behavior was poor sleep quality (Table 2). All health behaviors and chronic health conditions were significantly associated with CI. The sample consisted of mostly women (81.5%), individuals with 12 or less years of education, higher BMI levels, and those living in rural areas, which may present potential limitations. Conclusion Findings reveal lifestyle and environmental exposures relevant to CI and highlight the possible influence of behavioral and cultural factors on dementia development. As the first study to investigate the prevalence of CI and associated factors in Armenia, this research lays the foundations for understanding unmet needs for cognitive health, guiding future policy, and establishing sustainable health infrastructure in similar post‐Soviet, LMIC. Future research should be aimed at further investigating which risk factors are predictive of cognitive status and dementia development in the region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".