Computerized testing augments pencil-and-paper tasks in measuring HIV-associated mild cognitive impairment*
Bibliographic record
Abstract
BACKGROUND: Existing tools for rapid cognitive assessment in HIV-positive individuals with mild cognitive deficits lack sensitivity or do not meet psychometric requirements for tracking changes in cognitive ability over time. METHODS: Seventy-five nondemented HIV-positive patients were evaluated with the Montreal Cognitive Assessment (MoCA), a brief battery of standardized neuropsychological tests, and computerized tasks evaluating frontal-executive function and processing speed. Rasch analyses were applied to the MoCA data set and subsequently to the full set of data from all tests. RESULTS: The MoCA was found to adequately measure cognitive ability as a single, global construct in this HIV-positive cohort, although it showed poorer precision for measuring patients of higher ability. Combining the additional tests with the MoCA resulted in a battery with better psychometric properties that also better targeted the range of abilities in this cohort. CONCLUSION: This application of modern test development techniques shows a path towards a quick, quantitative, global approach to cognitive assessment with promise both for initial detection and for longitudinal follow-up of cognitive impairment in patients with HIV infection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".