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Record W2984325144 · doi:10.1097/jnc.0000000000000122

Cognitive Impairment Among Aging People Living With HIV on Antiretroviral Therapy: A Cross-Sectional Study in Hunan, China

2019· article· en· W2984325144 on OpenAlexaboutno aff
Xueling Xiao, Hui Zeng, Caiyun Feng, Hang Tan, Lanlan Wu, Hui Zhang, Mary‐Lynn Brecht, Honghong Wang, Deborah Koniak‐Griffin

Bibliographic record

VenueJournal of the Association of Nurses in AIDS Care · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsnot available
FundersFogarty International Center
KeywordsCognitionCross-sectional studyAntiretroviral therapyMedicineGerontologyCognitive impairmentPopulationHuman immunodeficiency virus (HIV)ChinaClinical psychologyPsychologyPsychiatryFamily medicineEnvironmental healthViral load

Abstract

fetched live from OpenAlex

Our cross-sectional study examined the prevalence of cognitive impairment among people living with HIV (PLWH) aged 60 years or older. The sample, composed of 250 PLWH, was recruited from 2 clinics in Hunan, China. Structured questionnaires guided face-to-face interviews, including items addressing demographic characteristics, regimens of antiretroviral therapy, and cognitive status as measured by the Montreal Cognitive Assessment. Findings revealed cognitive function of this population was significantly lower than that of uninfected individuals based on historical comparisons; 87.2% (n = 218) of PLWH in our study had cognitive impairment. Global cognitive function as well as the domains of language and orientation decreased with age. Global cognitive function was associated with sex and education, but not with antiretroviral therapy regimens. These findings support an urgent need to include routine screening for cognitive function in older PLWH and the need to consider the complexity of the evaluation process.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.290
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2019
Admission routes1
Has abstractyes

Explore more

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