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 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 | 0.091 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".