Exploring the Utility of the Montreal Cognitive Assessment to Detect HIV-Associated Neurocognitive Disorder: The Challenge and Need for Culturally Valid Screening Tests in South Africa
Bibliographic record
Abstract
There is a strong need in South Africa for neuropsychological tests that can help detect HIV-associated neurocognitive disorder (HAND) in the country's 5.6 million people living with HIV. Yet South African neuropsychologists are challenged to do so, as few neuropsychological tests or batteries have been developed or adapted for, and normed on, South Africa's linguistically, culturally, educationally, and economically diverse population. The purpose of this study was to explore the utility of the Montreal Cognitive Assessment to detect HIV-associated neurocognitive impairment among a sample of HIV+ and HIV- Black, Xhosa-speaking South Africans. HIV+ participants performed significantly worse overall and specifically in the domains of visuospatial, executive, attention, and language (confrontation naming). Regression analysis indicated that HIV status and education were the strongest predictors of total scores. Floor effects were observed on cube drawing, rhinoceros naming, serial 7s, and one abstraction item, suggesting those items might not be useful in this population. While the Montreal Cognitive Assessment holds promise to help detect HAND in South Africa, it will likely need modification before it can be normed and validated for this population. Findings from this study may help neuropsychologists working with similar populations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".