P2‐452: APPLICABILITY OF THE KISWAHILI VERSION OF MONTREAL COGNITIVE ASSESSMENT AMONG OLDER ADULTS WITH VERY LITTLE AND NO EDUCATION IN RURAL TANZANIA: A VALIDATION SURVEY
Bibliographic record
Abstract
The incidence of dementia in the low and middle-income countries, particularly sub-Saharan Africa is rising [1,2]. The Montreal Cognitive Assessment (MoCA) has been widely used to screen for pre-clinical and clinical stage of this dementia. However, its use in Tanzania is very limited. The aim of this study was to investigate the applicability and psychometrics of the Kiswahili version of MoCA (K-MoCA) among older adult in the rural Tanzania. The K-MoCA was administered to 259 community living older adults in Chamwino district, together with the IDEA cognitive screening, IDEA-Instrumental Activities of Daily Living, and Mental Health Inventory as referencing instruments to examine its concurrent and construct validity. The IDEA was a more validated cognitive test for the Tanzanian population [3]. A subsample (n = 86) were diagnosed by a psychiatrist as 19 having normal cognition, 42 having MCI and 25 having dementia for examining its sensitivity and specificity. The reliability, and correlation of the K-MoCA with IDEA cognitive screen were also investigated. K-MoCA demonstrated an acceptable reliability (Cronbach alpha= 0.78) and was significantly correlated with the IDEA cognitive screen (r = 0.651, p < 0.001). Referencing to IDEA scores and psychiatrist rating, the K-MoCA total and the domain scores except abstraction and delayed recall, were significantly different between subjects with normal cognition, MCI and dementia; which indicated satisfactory discriminant validity (Table 1). Table 2 shows the construct validity of K-MoCA where the cognitive scores converges with the predicted relationship with age, education and instrumental ADL ability. However, the screening ability (Figure 1 and Figure 2), using the psychiatrist's rating as the golden standard, Receiver Operating Curve analysis, indicated that the recommended cut-off scores of 26 and 18 did not give acceptable specificities for detecting MCI (Sensitivity: 92%; specificity: 11) and dementia (Sensitivity: 92%; specificity: 55) respectively. The results may be related to the fact that some of the items were less culturally relevant to the Tanzanian population.
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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 | 0.003 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".