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Record W2742793493 · doi:10.1097/coh.0000000000000415

Features of cardiovascular disease in low-income and middle-income countries in adults and children living with HIV

2017· review· en· W2742793493 on OpenAlexaff
Andrew W. McCrary, Chidozie Nduka, Saverio Stranges, Gerald S. Bloomfield

Bibliographic record

VenueCurrent Opinion in HIV and AIDS · 2017
Typereview
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsWestern University
Fundersnot available
KeywordsDyslipidemiaMedicineDiseaseHuman immunodeficiency virus (HIV)Low and middle income countriesPopulationCoronary artery diseaseEnvironmental healthDeveloping countryGerontologyIntensive care medicineImmunologyPathologyPsychiatryEconomic growth

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The current article addresses crucial issues in identifying risk of cardiovascular disease (CVD) in people living with HIV in low-income and middle-income countries (LMICs). These issues are in need of urgent attention to advance our knowledge and inform actions to mitigate CVD in this population. We address CVDs in adults living with HIV as well as the unique aspects pertaining to children living with HIV (CLHIV), a group sorely under-represented in this field. RECENT FINDINGS: CVDs affecting adults such as hypertension, dyslipidemia, coronary artery disease, and heart failure, in addition to myocardial dysfunction, vascular diseases, and autoimmune phenomena are also being reported in CLHIV. In addition to the background disparity in prevalence of traditional CVD risk factors, it is also likely that differential access to antiretroviral treatment, the younger age of the HIV-infected population, and types of antiretroviral treatment commonly used in LMICs contribute to the observed differences. SUMMARY: Overall, the state of evidence for CVD in LMICs is limited and at times contradictory. We summarize the evidence with suggestions for high priorities for further scientific investigation. Now is the crucial time to intervene in modifying CVD risk in LMICs.

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)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.281
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.038
GPT teacher head0.345
Teacher spread0.307 · 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
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

Citations9
Published2017
Admission routes1
Has abstractyes

Explore more

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