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Record W3036631329 · doi:10.1038/s41572-020-0181-1

HIV and antiretroviral therapy-related fat alterations

2020· review· en· W3036631329 on OpenAlexaff
John R. Koethe, Claire Lagathu, Jordan E. Lake, Peré Domingo, Alexandra Calmy, Julian Falutz, Todd T. Brown, Jacqueline Capeau

Bibliographic record

VenueNature Reviews Disease Primers · 2020
Typereview
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsMcGill University Health Centre
FundersAgence Nationale de la Recherche
KeywordsLipodystrophyLipoatrophyMedicineStavudineAntiretroviral therapyImmunologyObesityAdipose tissueInternal medicineHuman immunodeficiency virus (HIV)Viral load

Abstract

fetched live from OpenAlex

Early in the HIV epidemic, lipodystrophy, characterized by subcutaneous fat loss (lipoatrophy), with or without central fat accumulation (lipohypertrophy), was recognized as a frequent condition among people living with HIV (PLWH) receiving combination antiretroviral therapy. The subsequent identification of thymidine analogue nucleoside reverse transcriptase inhibitors as the cause of lipoatrophy led to the development of newer antiretroviral agents; however, studies have demonstrated continued abnormalities in fat and/or lipid storage in PLWH treated with newer drugs (including integrase inhibitor-based regimens), with fat gain due to restoration to health in antiretroviral therapy-naive PLWH, which is compounded by the rising rates of obesity. The mechanisms of fat alterations in PLWH are complex, multifactorial and not fully understood, although they are known to result in part from the direct effects of HIV proteins and antiretroviral agents on adipocyte health, genetic factors, increased microbial translocation, changes in the adaptive immune milieu after infection, increased tissue inflammation and accelerated fibrosis. Management includes classical lifestyle alterations with a role for pharmacological therapies and surgery in some patients. Continued fat alterations in PLWH will have an important effect on lifespan, healthspan and quality of life as patients age worldwide, highlighting the need to investigate the critical uncertainties regarding pathophysiology, risk factors and management.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.039
GPT teacher head0.385
Teacher spread0.346 · 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
GenreReview

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

Citations217
Published2020
Admission routes1
Has abstractyes

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