MétaCan
Menu
Back to cohort
Record W2102951977 · doi:10.1093/cid/ciu919

End-Stage Renal Disease Among HIV-Infected Adults in North America

2014· article· en· W2102951977 on OpenAlexafffund
Alison G. Abraham, Yuezhou Jing, Michelle M. Estrella, C. William Wester, Heidi M. Crane, Joseph J. Eron, M. John Gill, Michael A. Horberg, Amy C. Justice, Marina B. Klein, Ángel M. Mayor, Richard D. Moore, Frank J. Palella, Chirag R. Parikh, Michael J. Silverberg, Elizabeth T. Golub, Lisa P. Jacobson, Sonia Napravnik, Gregory M. Lucas, Gregory D. Kirk, Constance A. Benson, Ann C. Collier, Steve Boswell, Chris Grasso, Kenneth H. Mayer, Robert S. Hogg, Richard Harrigan, Joan Montaner, Angela Cescon, John T. Brooks, Kate Buchacz, Kelly A. Gebo, John T. Carey, Benigno Rodríguez, Jennifer E. Thorne, James J. Goedert, Sean B. Rourke, Ann N. Burchell, Anita Rachlis, Robert F. Hunter-Mellado, Steven G. Deeks, Jeff Martin, M. J. Mugavero, James H. Willig, Mari M. Kitahata, Robert Dubrow, David A. Fiellin, T. R. Sterling, David W. Haas, Sally Bebawy, Megan Turner, Stephen J. Gange, Kathryn Anastos, Rosemary G. McKaig, Arthur M. Freeman, Carol Lent, Stephen E. Van Rompaey, Edvelyn Webster, L. Morton, Birgit Simon, Keri N. Althoff, Bryan Lau, Jing Zhang, Jiaojiao Jing, Shari Modur, David Hanna, Peter F. Rebeiro, Cherise Wong, A. Mendes

Bibliographic record

VenueClinical Infectious Diseases · 2014
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsMcGill UniversityUniversity of Calgary
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Minority Health and Health DisparitiesNational Center for Research ResourcesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Allergy and Infectious DiseasesNational Eye InstituteNational Cancer InstituteNational Institute on Alcohol Abuse and AlcoholismCanadian Institutes of Health ResearchNational Institute on Drug AbuseU.S. Public Health ServiceNational Institutes of Health
KeywordsMedicineEnd stage renal diseaseDiabetes mellitusIncidence (geometry)DialysisPoisson regressionInternal medicineCohortHemodialysisCoinfectionDiseaseConfidence intervalCohort studyHepatitis CImmunologyHuman immunodeficiency virus (HIV)PopulationEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Human immunodeficiency virus (HIV)-infected adults, particularly those of black race, are at high-risk for end-stage renal disease (ESRD), but contributing factors are evolving. We hypothesized that improvements in HIV treatment have led to declines in risk of ESRD, particularly among HIV-infected blacks. METHODS: Using data from the North American AIDS Cohort Collaboration for Research and Design from January 2000 to December 2009, we validated 286 incident ESRD cases using abstracted medical evidence of dialysis (lasting >6 months) or renal transplant. A total of 38 354 HIV-infected adults aged 18-80 years contributed 159 825 person-years (PYs). Age- and sex-standardized incidence ratios (SIRs) were estimated by race. Poisson regression was used to identify predictors of ESRD. RESULTS: HIV-infected ESRD cases were more likely to be of black race, have diabetes mellitus or hypertension, inject drugs, and/or have a prior AIDS-defining illness. The overall SIR was 3.2 (95% confidence interval [CI], 2.8-3.6) but was significantly higher among black patients (4.5 [95% CI, 3.9-5.2]). ESRD incidence declined from 532 to 303 per 100 000 PYs and 138 to 34 per 100 000 PYs over the time period for blacks and nonblacks, respectively, coincident with notable increases in both the prevalence of viral suppression and the prevalence of ESRD risk factors including diabetes mellitus, hypertension, and hepatitis C virus coinfection. CONCLUSIONS: The risk of ESRD remains high among HIV-infected individuals in care but is declining with improvements in virologic suppression. HIV-infected black persons continue to comprise the majority of cases, as a result of higher viral loads, comorbidities, and genetic susceptibility.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.301
Teacher spread0.287 · 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 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

Citations172
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueClinical Infectious DiseasesSame topicHIV/AIDS drug development and treatmentFrench-language works237,207