NEU Screen Shows High Accuracy in Detecting Cognitive Impairment in Older Persons Living With HIV
Bibliographic record
Abstract
The NEUrocognitive (NEU) Screen is a practical tool proposed to screen for HIV-associated cognitive impairment in the clinical setting. This is a pencil-and-paper method that can be applied rapidly (≤10 minutes for administration) and has no copyright limitations. In this study, we aimed at investigating its diagnostic accuracy in an older population of persons living with HIV (PLWH), with cutoffs set at 30, 40, 50, and 60 years. Data were collected from a sample of 368 PLWH who underwent a comprehensive neuropsychological tests battery (gold standard). Results of statistical tests showed that accuracy of the NEU Screen increased with age of the participants. The highest degree of precision, with a sensitivity of 91% and specificity of 92%, was obtained for people ages 60 years or older (correct classification: 91%). These optimal results point to the great potential of the NEU Screen as a tool for detecting cognitive disorders in older PLWH.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".