Semantic memory navigation in HIV: Conceptual associations and word selection patterns
Bibliographic record
Abstract
Objective: This proof-of-concept study aimed to characterize semantic memory profiles in individuals with human immunodeficiency virus (HIV) and mild neurocognitive impairment. Method: Using a semantic relatedness task, we explored conceptual association and word selection patterns in people living with HIV (PLWH; n = 50) relative to people living without HIV (n = 46). We also studied whether word selection patterns in the PLWH group were associated with working memory capacity, cognitive flexibility and inhibitory control. Results: While accuracy did not differ between groups, PLWH produced significantly longer responses than controls (r = .32), with fewer hypernyms (d = .47), more troponyms (r = .37), and words that were more frequent (r = .39) and had more phonological neighbors (r = .22). These patterns survived covariation with participants’ cognitive status. None of these patterns correlated with measures of working memory, cognitive flexibility, inhibitory control or viral load (all correlation coefficients < .36). Conclusions: Together, these results suggest that PLWH might use alternative word finding strategies during semantic memory navigation, irrespective of the severity of other cognitive symptoms. Such findings contribute to the characterization of cognitive deficits in HIV and to the search for novel markers of the condition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".