Predicting occupational outcomes from neuropsychological test performance in older people with HIV
Bibliographic record
Abstract
OBJECTIVE: The ability to work is amongst the top concerns of people living with well treated HIV. Cognitive impairment has been reported in many otherwise asymptomatic persons living with HIV and even mild impairment is associated with higher rates of occupational difficulties. There are several classification algorithms for HIV-associated neurocognitive disorder (HAND) as well as overall scoring methods available to summarize neuropsychological performance. We asked which method best explained work status and productivity. DESIGN: Participants (N = 263) drawn from a longitudinal Canadian cohort underwent neuropsychological testing. METHODS: : Several classification algorithms were applied to establish a HAND diagnosis and two summary measures (NPZ and Global Deficit Score) were computed. Self-reported work status and productivity was assessed at each study visit (four visits, 9 months apart). The association of work status with each diagnostic classification and summary measure was estimated using logistic regression. For those working, the value on the productivity scale was regressed within individuals over time, and the slopes were regressed on each neuropsychological outcome. RESULTS: The application of different classification algorithms to the neuropsychological data resulted in rates of impairment that ranged from 28.5 to 78.7%. Being classified as impaired by any method was associated with a higher rate of unemployment. None of the diagnostic classifications or summary methods predicted productivity, at time of testing or over the following 36 months. CONCLUSION: Neuropsychological diagnostic classifications and summary scores identified participants who were more likely to be unemployed, but none explained productivity. New methods of assessing cognition are required to inform optimal workforce engagement.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".