Abnormal cognitive aging in people with HIV: evidence from data integration between two countries’ cohort studies
Bibliographic record
Abstract
OBJECTIVES: Previous research has shown inconsistent results on whether cognitive aging is abnormal in people with HIV (PWH) because of low sample size, cross-sectional design, and nonstandard neuropsychological methods. To address these issues, we integrated data from two longitudinal studies: Australian HIV and Brain Ageing Research Program ( N = 102) and CNS HIV Antiretroviral Therapy Effects Research (CHARTER) study ( N = 924) and determined the effect of abnormal aging on neurocognitive impairment (NCI) among PWH. METHODS: Both studies used the same neuropsychological test battery. NCI was defined based on demographically corrected global deficit score (≥0.5 = impaired). Both studies also assessed comorbidities, neuropsychiatric conditions and functional status using similar tools. To determine the cross-sectional and longitudinal effects of age on the risk of NCI, a generalized linear mixed-effect model tested main and interaction effects of age group (young, <50 vs. old, ≥50) and time on NCI adjusting the effects of covariates. RESULTS: Older PWH had 83% higher chance of NCI compared with younger PWH [odds ratio (OR) = 1.83 (1.15-2.90), P < 0.05]. Older participants also had a greater risk of increases in NCI over the follow-up [OR = 1.66 (1.05-2.64), P < 0.05] than younger participants. Nonwhite ethnicity ( P < 0.05), having a contributing ( P < 0.05) or confounding ( P < 0.001) comorbidity, greater cognitive symptoms ( P < 0.001), and abnormal creatinine level ( P < 0.05), plasma viral load greater than 200 copies/ml ( P < 0.05), being from the Australian cohort ( P < 0.05) were also associated with a higher risk of NCI. CONCLUSION: Data integration may serve as a strategy to increase sample size and study power to better assess abnormal cognitive aging effect in PWH, which was significant in the current study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".