Cognitive screening considerations for psychosocial clinical trials in HIV, aging, and cognition
Bibliographic record
Abstract
Cognitive impairment is a common comorbidity among individuals aging with HIV, which can be an extreme source of stress and anxiety for many. Psychosocial interventions have the potential to alleviate symptoms associated with cognitive impairment and help improve the quality of life of people with HIV as they continue to age; these interventions are in the infancy of development and require further testing via clinical trials. The slow development of interventions may be partially attributed to a common trend of requiring a formal HIV-associated neurocognitive disorder diagnosis to qualify for psychosocial clinical trials. HIV-associated neurocognitive disorder is diagnosed through intensive, time-consuming tests, and still many cases of HIV-associated neurocognitive disorder remain undiagnosed, misdiagnosed, or misclassified due to the limitations of the assessment process. This commentary suggests an alternate method of screening for cognitive impairments through the use of a brief, low-barrier assessment, alongside validity considerations. Such alternate screening may improve enrollment and completion rates in psychosocial clinical trials for people aging with HIV and cognitive impairment, by removing the burden of extensive testing that is commonly associated with an HIV-associated neurocognitive disorder diagnosis from clinical trial eligibility, while still providing valuable insight into individuals' cognitive functioning.
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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 | 0.310 | 0.560 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.023 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".