Cognitive impairment and neurocognitive profiles among people living with HIV and HIV- negative individuals older over 50 years: a comparison of IHDS, MMSE and MoCA
Bibliographic record
Abstract
Abstract Background The aim of our study was to examine potential differences in the assessment of neurocognitive impairment (NCI) using cognitive screening tools between PLWH and HIV-negative individuals, as well as to compare the neurocognitive profiles. Methods This was baseline evaluation of Pudong HIV Aging Cohort, including 465 people living with HIV (PLWH) and 465 HIV-negative individuals aged over 50 years matched by age (± 3 years), sex and education. NCI was assessed using the Chinese version of Mini-mental State Examination (MMSE), the International HIV Dementia Scale (IHDS) and Beijing version of Montreal Cognitive Assessment (MoCA). Results In total, 258 (55.5%), 91 (19.6%), and 273 (58.7%) of PLWH were classified as having NCI by the IHDS, MMSE, and MoCA, compared to 90 (19.4%), 25 (5.4%), 135 (29.0%) of HIV-negative individuals, respectively (all p < 0.05); such associations Only MMSE revealed sex difference in NCI prevalence among PLWH. PLWH showed a larger overlap of NCI detected by IHDS, MMSE, and MoCA than HIV-negative people. Regarding cognitive domains, IHDS-motor and psychomotor speeds and MoCA-executive function showed the greatest disparities between two groups. In multivariable analysis, older age and more depressive symptoms were positively associated with NCI regardless of the screening tools or HIV serostatus. Conclusion PLWH display a higher prevalence of NCI and distinct neurocognitive profiles compared to HIV-negative individuals, despite viral suppression. Our data support that older PLWH tend to have deficits in multiple cognitive domains simultaneously. It is advisable to utilize the cognitive screening tools in conjunction to reveal complex patterns of cognitive deficits among PLWH, especially older PLWH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".