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Neurological Complications of HIV Infection

2025· book-chapter· en· W4412693862 on OpenAlexaboutno aff
Rodrigo Hasbun, Joseph S. Kass

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)MedicineVirology

Abstract

fetched live from OpenAlex

Abstract HIV-associated neurocognitive disorder (HAND) is highly prevalent among both PWH who are antiretroviral treatment (ART)-naive, as well as PWH taking ART with virologic suppression. HAND is associated with significant cognitive, behavioral, and motor abnormalities that can impact ART adherence, virologic success, retention in care (especially for older individuals), and quality of life. Rapid screening tools such as the Montreal Cognitive Assessment test and the Frontal Assessment Battery test have been evaluated for their use in diagnosing HAND in the clinic. For people with HIV (PWH) who are presenting with signs or symptoms of meningitis, the differential diagnosis is very broad (e.g., viral, bacterial, fungal, mycobacterial, or lymphomatous). All adults with meningitis should be screened for HIV. Meningitis in PWH is usually treatable, and the cause should be thoroughly investigated. A meta-analysis of studies of meningitis in PWH in Africa documented that the three most common causes were C. neoformans, M. tuberculosis, and bacterial meningitis. This chapter delineates the clinical features, differential diagnosis, and management of the neurologic effects of HIV, including HAND, meningitis, myelopathy, distal symmetric polyneuropathy (DSPN), inflammatory demyelinating polyneuropathy, neurologic complications of cytomegalovirus (CMV) infections, and intracranial lesions.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.045
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0450.015

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 head0.027
GPT teacher head0.276
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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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