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Record W3197919879 · doi:10.1097/coh.0000000000000703

Renal adverse drug reactions

2021· review· en· W3197919879 on OpenAlexaff
Christine S. Hughes

Bibliographic record

VenueCurrent Opinion in HIV and AIDS · 2021
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineTenofovir alafenamideAdverse effectRenal functionKidney diseaseInternal medicineNephrotoxicityIntensive care medicineHuman immunodeficiency virus (HIV)KidneyAntiretroviral therapyImmunologyViral load

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Chronic kidney disease (CKD) is common in people living with HIV (PLWH) and is related to a multitude of factors. The aim of this review is to provide an overview of the most recent evidence of renal adverse effects of antiretroviral drugs, predictors of CKD risk and areas for future research. RECENT FINDINGS: Advancing age, cardiometabolic risk factors and adverse effects of antiretroviral drugs contribute to the higher prevalence of CKD in PLWH. Genetic factors and baseline clinical CKD risk are strongly correlated to risk of incident CKD, although it is unclear to what extent gene polymorphisms explain renal adverse effects related to tenofovir disoproxil fumarate (TDF). Switching from TDF to tenofovir alafenamide (TAF) in people with baseline renal dysfunction improves renal parameters; however, the long-term safety and benefit of TAF in individuals at low risk of CKD is an area of ongoing research. SUMMARY: Several factors contribute to estimated glomerular function decline and CKD in PLWH. Clinical risk scores for CKD may be useful to inform selection of ART in an ageing population. In people with baseline renal dysfunction, potentially nephrotoxic antiretroviral drugs should be avoided.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score1.000

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 head0.084
GPT teacher head0.389
Teacher spread0.305 · 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.

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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

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