Personalized Risk Index for Neurocognitive Decline Among People With Well-Controlled HIV Infection
Bibliographic record
Abstract
BACKGROUND: Little is known about the predictors of neurocognitive decline in HIV+ individuals with good virological control. Identification of modifiable risk factors would allow targeted interventions to reduce the risk of decline in higher risk individuals. The objective of this study was to develop a risk index to predict neurocognitive decline over 3 years in aviremic HIV+ individuals. METHODS: As part of the CNS HIV Anti-Retroviral Therapy Effects Research (CHARTER) study, HIV+ adults completed clinical evaluation and neuropsychological tests every 6 months. Group-based trajectory analysis was used to detect patterns of neurocognitive change; individuals who deteriorated ≥ 0.5 SD on at least one neuropsychological test were considered decliners. Multiple logistic regression was used to identify baseline sociodemographic, clinical, biological, and lifestyle factors associated with decline in the subgroup that was consistently aviremic during the first 3 years. A risk index was developed using the beta-coefficients from the final regression model. RESULTS: Neurocognitive decline occurred in 23 of 191 (12%) participants followed longitudinally. The baseline factors that predicted decline were glomerular filtration rate ≤50 mL/min, known duration of HIV infection ≥15 years, education ≤12 years, and cerebrospinal fluid protein >45 mg/dL. CONCLUSIONS: Using this analytic approach, neurocognitive decline was uncommon in this sample of aviremic HIV+ individuals. The 3-year risk of decline ranged from 2% in those with no risk factors to 95% in those with all 4. The strongest predictor was glomerular filtration rate, also a predictor of cardiovascular disease. This raises the possibility that controlling vascular risk factors could reduce the risk of neurocognitive decline.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".