Montreal Cognitive Assessment Performance in HIV/AIDS: Impact of Systemic Factors
Bibliographic record
Abstract
BACKGROUND: A large proportion of people living with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) suffer from neurocognitive impairment (NCI). The causes of the NCI are multifold in HIV infection although a subset of HIV/AIDS patients are affected by the spectrum syndrome, HIV-associated neurocognitive disorder (HAND). We investigated the Montreal Cognitive Assessment (MoCA) in relation to clinical, demographic and laboratory findings as well as its ability to predict symptomatic HAND (sHAND) among patients with HIV/AIDS. METHODS: All subjects were receiving regular HIV care including CD4+ T cell counts, plasma viral load measurements, clinical evaluations and antiretroviral therapy. The diagnosis of sHAND was based upon clinical, neuroimaging, and neuropsychological assessments. RESULTS: Among HIV-1 seropositive subjects (n=125), ethnicity, education and employment were positively correlated with their MoCA scores (p<0.05). In contrast, polypharmacy, central nervous system penetration-effectiveness (CPE) score, antiretroviral drug exposure, substance use and nucleoside/nucleotide reverse transcriptase inhibitor side effects were negatively correlated with MoCA scores (p<0.05). Of note, MoCA scores were not associated with CD4 T cell nadir levels, age, peak viral load, or veterans aging cohort study index. In subjects with or without sHAND, mean MoCA scores differed (sHAND, 22.8±3.51; non-HAND 25.2±2.64) (p<0.05) with a receiver operating characteristic curve showing an area under curve of 0.71 and an optimal MoCA cut-off value of 23.5 when compared to the established diagnostic paradigm. CONCLUSIONS: MoCA scores were generally lower in this HIV/AIDS population compared to reported scores in the general population. MoCA performance was associated with multiple clinical variables but displayed limited predictive utility in detecting sHAND.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".