Quantifying cognition at the bedside: a novel approach combining cognitive symptoms and signs in HIV
Bibliographic record
Abstract
BACKGROUND: Up to half of all people with HIV infection have some degree of cognitive impairment. This impairment is typically mild, but nonetheless often disabling. Although early detection of cognitive impairment offers the greatest hope of effective intervention, there are important barriers to this goal in most clinical settings. These include uncertainty about how self-reported cognitive symptoms relate to objective impairments, and the paucity of bedside measurement tools suitable for mild deficits. Clinicians need guidance in interpreting cognitive symptoms in this population, and a brief cognitive measurement tool targeted to mild impairment. We addressed these two problems together here. The objective of this study was to determine the extent to which performance on cognitive tests and self-reported cognitive symptoms form a unidimensional construct. METHODS: Two hundred three HIV+ individuals completed the Montreal Cognitive Assessment, computerized cognitive tasks and a questionnaire eliciting cognitive symptoms. Rasch measurement theory was applied to determine whether patient-reported and performance items could be combined to measure cognition as a unidimensional latent construct. RESULTS: Performance-based items and cognitive symptoms are arranged hierarchically along the same continuum of cognitive ability, forming a measure with thresholds covering a broad spectrum of ability that has good internal reliability. The cognitive symptoms that fit the measurement model relate to important aspects of everyday life, providing evidence that the identified construct is meaningful. CONCLUSIONS: This finding lays the foundation for a rapid measure of cognitive ability in people with HIV infection that is feasible for routine clinical use, and shows that some cognitive symptoms are systematically related to performance in this population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".