The concordance between the Montreal cognitive assessment and the repeatable battery for the assessment of neuropsychological status as a cognitive screening tool in a south African community sample
Bibliographic record
Abstract
Abstract We aimed to compare the concordance between the Montreal Cognitive Assessment (MoCA) and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), as cognitive screening tools to detect mild cognitive impairment (MCI) in a South African adult community sample ( N = 370). The MoCA showed acceptable internal consistency, agreement with the RBANS and good criterion-related validity. The MoCA demonstrated fair performance, compared to the RBANS, for predicting MCI, with AUCs of 0.711 (English) and 0782 (Afrikaans). Using the recommended cut-off score of 26/30, the MoCA showed high sensitivity but low specificity. Sensitivity and specificity were optimal when the cut-off scores were lowered to 25/30 (English) and 24/30 (Afrikaans). MoCA scores were significantly associated with language, sex, age and education. While these findings demonstrate applicability of the MoCA in screening for and identifying mild cognitive difficulty in this population, our findings suggest that modifications are needed to improve differentiating between normal aging and MCI. Until a culturally adapted version of the MoCA is developed and validated for this population we suggest lowering the cut-off score to 25/30 (English) and 24/30 (Afrikaans) to reduce false positive NCD diagnoses. Demographic factors (age, sex, language and education) also need to be considered.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".