Properties of a brief assessment tool for longitudinal measurement of cognition in people living with HIV
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment is common in chronic HIV infection and there is concern that it may worsen with age. Distinguishing static impairment from on-going decline is clinically important, but the field lacks well-validated cognitive measures sensitive to decline and feasible for routine clinical use. Measures capable of detecting improvement are also needed to assess interventions. The objective of this study is to estimate the extent of change on repeat administration of three different forms of a brief computerized cognitive assessment battery (B-CAM) developed for assessing cognitive ability in the mildly-impaired to normal range in people living with HIV. We hypothesized no change over a six-month period in people on effective antiretroviral therapy. METHODS: 102 HIV+ individuals completed a set of computerized cognitive tasks on three occasions over a six-month period. Rasch analysis was used to determine if change over time (i.e. improvement due to practice) was uniform across tasks and to refine scoring in order to produce three forms of the B-CAM of equivalent level of difficulty. Group-based trajectory analysis (GBTA) was then applied to determine if performance at baseline influenced the magnitude of practice-related improvement on the battery as a whole over the course of follow-up. RESULTS: Two cognitive tasks (fluency and word recall) had different levels of difficulty across test sessions, related to the different forms of the tasks. These two items were split by testing session. For all other items, the level of difficulty remained constant across all three time points. GBTA showed that the sample was composed of three distinct groups of people with unique trajectories, defined mainly by level of cognitive ability at baseline. Only the highest group showed an apparent improvement over time, but this change fell within measurement error. CONCLUSIONS: Rasch analysis provides mathematical confirmation that these three forms of the B-CAM are of equivalent difficulty. GBTA demonstrates that no adjustment of the total score is required to correct for practice effects. Application of these modern statistical methods paves the way towards rapid and robust quantification of change in cognition.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".