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Record W2070055611 · doi:10.1097/tgr.0000000000000002

Cognitive Consequences of Aging With HIV

2014· article· en· W2070055611 on OpenAlexaff
David E. Vance, Graham J. McDougall, Natalie Wilson, Marcus Otavio Debiasi, Shameka L. Cody

Bibliographic record

VenueTopics in Geriatric Rehabilitation · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsMcGill University
FundersNational Institute on Aging
KeywordsMedicineHuman immunodeficiency virus (HIV)CognitionGerontologyClinical psychologyPsychiatryVirology

Abstract

fetched live from OpenAlex

Combination active antiretroviral therapy prevents HIV from replicating and ravaging the immune system, thus allowing people to age with this disease. Unfortunately, the synergistic effects of HIV and aging can predispose many to become more at-risk of developing cognitive deficits which can interfere with medical management, everyday functioning, and quality of life. The purpose of this article is to describe the role of cognitive reserve and neuroplasticity on cognitive functioning in those aging with this disease. Specifically, the role of environment and the health of these individuals can compromise cognitive functioning. Fortunately, some cognitive interventions such as prevention and management of co-morbidities, cognitive remediation therapy, and neurotropic medications may be of value in preventing and rehabilitating the cognitive consequences of aging with HIV. Novel approaches such as cognitive prescriptions, transcranial direct stimulation, and binaural beat therapy may also be considered as possible techniques for cognitive rehabilitation.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.267
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2014
Admission routes1
Has abstractyes

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