Neurocognitive screening tools in HIV/AIDS: comparative performance among patients exposed to antiretroviral therapy
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to compare the performance of several bedside neuropsychological tools for detection of HIV-associated neurocognitive disorder (HAND) in antiretroviral drug-exposed persons. METHODS: We analysed the relative performance of the HIV Dementia Scale (HDS), International HIV Dementia Scale (IHDS) and the Mini-Mental Status Exam (MMSE) together with neuropsychological tests (Symbol-Digit, Grooved Pegboard and Trail Making) in HIV-1-seronegative subjects (HIV-; n=13) and in HIV-1-seropositive subjects with HAND (HIV+HAND; n=13) and other neurological disorders (HIV+OND; n=20). RESULTS: Established neuropsychological tests consistently showed significantly poorer performance by HIV+HAND subjects compared with the other two groups. Similarly, the mean HDS and IHDS scores were lower in the HIV+HAND group compared with the other two groups (P<0.005) while the mean MMSE score did not show significant differences between the HIV+HAND and HIV+OND groups. Receiver operator characteristics curves generated from these data using predefined cut-off scores revealed that the HDS, IHDS and MMSE displayed corresponding area under the curve values of 0.82, 0.74 and 0.48, respectively (P<0.006). CONCLUSIONS: The present findings indicate that the MMSE is a weak tool for diagnosing HAND in this group of patients but the HDS and IHDS demonstrate better efficiencies, although cut-off values for the HDS require reassessment in the era of effective antiretroviral therapy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".