Voluntary cognitive screening: characteristics of participants in an Asian setting
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) and dementia are reaching epidemic proportions in Asia. Lack of awareness and late presentation are major obstacles to early diagnosis and timely intervention. Cognitive screening may be an effective method for early detection of dementia in Asia. The purpose of this work was to study the characteristics of subjects volunteering for cognitive screening in an Asian setting and to determine the prevalence of MCI. METHODS: Retrospective and cross-sectional data from community subjects attending a screening program from 2008 to 2013 were analyzed. Information on demographics, vascular risk factors, subjective symptoms, and cognitive measures were analyzed over the 6-year period. RESULTS: Over the 6 years from 2008 to 2013, 1,243 community subjects voluntarily turned up for cognitive screening (91.2% were Chinese, 5.23% were Indian, 1.37% were Malay, and 2.25% were Eurasian). The mean age of the participants was 61.3 years and the mean number of years of education was 11.0 years. A total of 71.1% of participants were living in public housing, 59.8% had at least one cardiovascular risk factor, and 56.2% reported subjective cognitive symptoms. Over a period of 6 years, no significant change in demographic or clinical variables was noted. High cholesterol and hypertension were consistently the top two risk factors found in the population screened. In total, 17.2% of the total cohort had MCI. Across the 6 years, the proportion with MCI and depression was relatively constant. CONCLUSION: A significant proportion of participants attending voluntary cognitive screening have MCI. Low level of education and presence of vascular risk factors are general predisposing characteristics for MCI, and there are more specific factors pertaining to sex and employment status.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".