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Record W2802589557 · doi:10.1097/md.0000000000010380

Prevalence and correlates of chronic kidney disease (CKD) among ART-naive HIV patients in the Niger-Delta region of Nigeria

2018· article· en· W2802589557 on OpenAlexaff
Udeme E. Ekrikpo, André Pascal Kengne, Emmanuel Effa, Aminu K. Bello, John Ekott, Cindy George, Babatunde Lawal Salako, Ikechi G. Okpechi

Bibliographic record

VenueMedicine · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineKidney diseaseRenal functionInternal medicineLogistic regressionEpidemiologyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

Widespread use of antiretroviral therapy (ART) in human immunodeficiency virus (HIV) patients has led to improved longevity with the attendant increase in noncommunicable disease prevalence including chronic kidney disease (CKD). This study documents the prevalence of CKD in a large HIV population in Southern Nigeria.This is a single center, 15-year analysis in ART-naïve patients. CKD was defined as the occurrence of estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m on 2 consecutive occasions 3 to 12 months apart using the chronic kidney disease epidemiology collaboration (CKD-EPI) equation. The Cochran-Armitage and Cuzick tests were employed to assess for trend across the years for CKD prevalence and CD4 count, respectively. Multivariable logistic regression models were used to identify independent associations with CKD.In all, 1317 patients (62.2% females) with mean age of 34.5 years and median CD4 count of 194 cells/μL were included. CKD prevalence was 13.4% (95%CI 11.6%-15.4%) using the CKD-EPI equation (without the race factor). Multivariable analysis identified increasing age and CD4 count <200 cells/μL as being independently associated with CKD occurrence.This study reports a high prevalence of CKD in ART-naïve HIV-infected patients. Measures to improve diagnosis of kidney disease and ensure early initiation of treatment should be integrated in HIV treatment programmes in this setting.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.014
GPT teacher head0.285
Teacher spread0.271 · 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
GenreEmpirical

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

Citations21
Published2018
Admission routes1
Has abstractyes

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