Validation of the Montreal cognitive assessment against the RBANS in a healthy South African cohort
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) represents an intermediate state between normal cognition and dementia. Early detection and treatment of reversible contributing factors to progressive cognitive decline currently forms the cornerstone of management. As the population at risk of developing dementia is projected to increase significantly in many low- and middle-income countries where health care services continue to operate under clinical and human resource constraints, there is a need for low-cost, quick and reliable screening tools. The Montreal cognitive assessment (MoCA) was developed as a brief screening tool with high sensitivity and specificity for detecting MCI. The initial validation sample for the MoCA consisted of English and French speaking Canadians. Studies undertaken in a variety of countries show that the reliability and validity of the MoCA in screening for MCI is good; however, it has been recommended that some item modification and adjustment of cut-offs for the diagnosis of MCI in these populations may be needed to account for cultural differences.To date, no studies have evaluated the MoCA in the South African population. We aimed to compare the validity of the MoCA to the RBANS, evaluate the effectiveness of the MoCA as a screening tool for MCI and generate normative data for the MoCA. METHODS: A cross-sectional observational study comprising a sample of 370 cognitively healthy males and females aged 18 years and older of mixed race (Coloured ethnicity) who were administered the MoCA and RBANS during screening. RESULTS: = 0.000), indicating good criterion-related validity. The MoCA also showed good agreement with the RBANS according to the Bland-Altman plot. ROC statistics demonstrated that the performance of the MoCA for predicting MCI compared to the RBANS was fair with an AUC of 0.794. Using the recommended cut-off score of 26, the MoCA showed high sensitivity (94.23%) but low specificity (28.16%). When the cut-off score was lowered to 23, the sensitivity was 75% and specificity 66.77%, while a cut-off of 24 demonstrated a sensitivity of 84.62% and a specificity of 52.53%. CONCLUSION: Although the MoCA appears fairly reliable at identifying MCI in this population, our findings suggest that some modification to certain domains and items is needed to improve the differentiation between normal ageing and MCI. Until such time that a culturally adapted version of the MoCA has been developed and validated for this population, we suggest lowering the cut-off score to 24 in order to reduce false-positive diagnoses of MCI.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".