Montreal Cognitive Assessment 5‐minute protocol is accurate in screening for mild cognitive impairment in the rural African population
Bibliographic record
Abstract
Abstract Background The prevalence of dementia in Tanzania, as in other developing countries is progressively increasing.1 Yet the screening instruments for the pre‐clinical stage of the diseases are lacking. This study examined the diagnostic accuracy of the Montreal Cognitive Assessment‐5‐minutes protocol (MoCA‐5‐min) among older adult in the rural Tanzania. Methods After cultural adaptation following Brislin’s approach,2 the MoCA‐5‐min and the IDEA cognitive screening were concurrently administered to community‐dwelling older adults (n=202) in Chamwino district and 40 re‐evaluated at 6 weeks. Exploratory factor analysis (EFA) using principal component method and oblique rotation was performed to determine the underlying factor structure of the scale. The concurrent and the diagnostic accuracy of the MoCA‐5‐min were examined by comparing its score with IDEA cognitive screening and the psychiatrist’s diagnosis using DSM‐V criteria respectively. Results The EFA found that all the MoCA‐5‐min items highly loaded into one component, with factor loading ranging from 0.550 to 0.879. The intraclass correlation coefficient for 6 weeks test‐retest reliability was 0.85. Its strong significant correlation with the IDEA screening (Pearson's r = 0.614, p < 0.001) demonstrated a good concurrent validity. Using the psychiatrist’s rating as a gold standard, the area under the curve (AUC) was 0.861, (95% CI = 0.799 – 0.922) (Figure 1). With the optimal cut‐off score for MCI at 22, the sensitivity was 80% and specificity was 74%. As for dementia, at a score of 16 the sensitivity was 90% and specificity was 80%, whereas the AUC was 0.910, (95%CI = 0.852 – 0.967) (Figure 2). Upon stratifying the sample into different age groups, the optimal cut‐off scores tended to decrease with the increase in age (Table 1). Conclusion The MoCA‐5‐min is reliable and provides a valid and accurate measure of cognitive decline among older population in the rural settings of Tanzania. The use of varying cut‐off scores across age groups may ensure a more precise discriminatory power of the MoCA‐5‐min. References: 1. WHO. Dementia: Key facts [Internet]. World Health Organization ‐ Fact Sheets. 2019 [cited 2019 Nov 11]. p. 1–5. Available from: https://www.who.int/news‐room/fact‐sheets/detail/dementia . 2. Brislin RW. Back‐translation for cross‐cultural research. J Crosss Cult Psychol. 1970;1(3):185–216.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".