A better screening tool for HIV-associated neurocognitive disorders
Bibliographic record
Abstract
OBJECTIVE: Existing screening tools for HIV-Associated Neurocognitive Disorders (HAND) may lack the accuracy required for clinical use. We hypothesized that the diagnostic accuracy of the Montreal Cognitive Assessment (MoCA) as a screening tool for HAND might be improved with a stronger scoring methodology. DESIGN: Two hundred HIV-positive participants aged 18-65 years completed the MoCA and a battery of neuropsychological tests. METHODS: HAND diagnosis was established according to the Frascati criteria, and an NPZ-8 score was also calculated. Rasch analysis was applied to the MoCA items to create a quantitative score. RESULTS: The optimal cut-off on the quantitative MoCA for detecting impairment as per Frascati criteria yielded a sensitivity of 0.74 and a specificity of 0.68. Overall accuracy was 0.79 (95% CI: 0.73-0.85), an improvement over standard scoring methods. However, whether cognition was quantified with the quantitative MoCA or with NPZ-8, there was substantial overlap between diagnostic categories; several individuals categorized as impaired had better overall cognitive function as assessed by NPZ-8 or quantitative MoCA than those classified as normal using standard criteria. CONCLUSION: Quantifying performance on MoCA items through Rasch analysis improves its accuracy as a screening tool for HAND, and demonstrates that cognition can be measured as a unidimensional construct in HIV, at least at the level of precision of bedside testing. However, the current categorical diagnostic approach to HAND is poorly aligned with summary measures of cognitive ability. Measuring cognition as a quasi-continuous construct may be more relevant than conventional HAND diagnostic categories for many clinical purposes.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".