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Record W2745017484 · doi:10.1097/coh.0000000000000406

Hypertension in people living with HIV

2017· review· en· W2745017484 on OpenAlexaff
Rosan A van Zoest, Bert‐Jan H. van den Born, Peter Reiss

Bibliographic record

VenueCurrent Opinion in HIV and AIDS · 2017
Typereview
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsInstitute of Infection and Immunity
FundersViiV HealthcareZonMwAids FondsGilead SciencesBristol-Myers Squibb
KeywordsMedicineIntensive care medicineHuman immunodeficiency virus (HIV)Antiretroviral therapyObesityImmunologyInternal medicineViral load

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review describes what is known concerning the burden of hypertension among people living with HIV (PLHIV), and also addresses relevant topics with respect to its risk factors and clinical management. RECENT FINDINGS: Hypertension is highly prevalent in HIV-positive populations, and may be more common than in HIV-negative populations. Risk factors contributing to the development of hypertension in PLHIV include demographic factors, genetic predisposition, lifestyle, comorbidities such as obesity, antiretroviral therapy-related changes in body composition, and potentially also immunodeficiency, immune activation and inflammation, as well as effects from antiretroviral therapy itself. Clinical management of hypertension in PLHIV requires awareness for drug-drug interactions between antiretroviral drugs and antihypertensive drugs. Awareness, treatment, and control of hypertension in PLHIV is currently suboptimal and should be improved. SUMMARY: The burden of hypertension among PLHIV is high and its pathophysiology most likely multifactorial. Elucidating the exact pathophysiology of hypertension in PLHIV is vital as this may provide new targets to impact and improve clinical management. In the meantime, efforts should be made to improve hypertension management as per existing clinical guidelines in order to safeguard cardiovascular health and quality of life in PLHIV.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.934
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.144
GPT teacher head0.418
Teacher spread0.274 · 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 designOther design
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

Citations82
Published2017
Admission routes1
Has abstractyes

Explore more

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