Exploration of demographic prevalence of mild cognitive impairment using Montreal cognitive assessment: A cross-sectional pilot study in the Cape Coast Metropolis, Ghana.
Bibliographic record
Abstract
Background: The Global prevalence of dementia is projected to rise, particularly in low and middle-income countries like Ghana. Mild cognitive impairment (MCI), an intermediate phase between normal cognitive aging and dementia, is characterized by an objective and subjective decline in cognitive abilities. Individuals with MCI have a greater likelihood of progression to dementia. Purpose: There is a paucity of studies focused on assessing the prevalence, risk factors and characteristics of mild cognitive impairment within the Ghanaian population. This study assessed the prevalence of mild cognitive impairment and explored its relationship with various sociodemographic factors. Methods: A prospective cross-sectional analytical study within Cape Coast, Ghana, evaluating the cognition of 100 participants using the Montreal Cognitive Assessment (MoCA) tool. The prevalence of MCI was determined using simple descriptive measures. The two-way ANOVA was used to determine risk factors for developing MCI. The Pearson correlation coefficient was used to determine the relationship between educational level and MoCA score. Results: A majority (65.4 %) of participants within the age group 40-49 years had mild cognitive impairment. 42.86 % of male and 40.54 % of female participants had MCI (MoCA score < 26). There was a significant correlation (r= 0.608, p= 0.0001) between the educational level of participants and the MoCA score. Participants classified as having MCI based on their MoCA score, performed significantly poorer in visuospatial, attention, language, abstraction and delayed recall domains compared to those with normal cognition. Conclusion: The MoCA tool is a useful for detecting MCI, particularly among Ghanaians with at least 7 years of formal education. The prevalence of MCI among individuals aged 40-49 years in the Cape Coast Metropolis represents an important health burden.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".