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Record W4411922993 · doi:10.1016/j.ekir.2025.06.044

Rationale and Design of the International Prospective Study of CKD of Uncertain Etiology in Agricultural Communities

2025· article· en· W4411922993 on OpenAlexaff
Jill Lebov, Daniel R. Brooks, Anna Aceituno, Hildaura Acosta, Shuchi Anand, Aurora Aragón, Mariela Arias-Hidalgo, Vivek Bhalla, Karen Courville, Jennifer Crowe, Idalina Cubilla‐Batista, Lawrence S. Engel, Nora Franceschini, David J. Friedman, Ramón Gárcía-Trabanino, Marvin González-Quiroz, Balaji Gummidi, Carolina Guzmán-Quilo, Vivekanand Jha, Bonnie R. Joubert, Karen Kesler, Adeera Levin, Indiana Mercedes López-Bonilla, Susan R. Mendley, Sumit Mohan, Ana Navas‐Acién, Afshin Parsa, Peter Rohloff, Emmanuel Jarquin Romero, Clemens Ruepert, Vicente Sánchez-Polo, Madeleine K. Scammell, Karla Solano, Lillian Trochinski, Lex van Geen, Sushrut S. Waikar

Bibliographic record

VenueKidney International Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Environmental Health SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesFogarty International CenterNational Institutes of Health
KeywordsMedicineEtiologyAgricultureKidney diseaseIntensive care medicineProspective cohort studyDiseaseEnvironmental healthInternal medicineEcology

Abstract

fetched live from OpenAlex

Introduction: There has been an alarming increase in the incidence of a chronic kidney disease (CKD) of unknown etiology primarily affecting young individuals engaged in agricultural activities in Mesoamerica and South Asia. Despite extensive research over the past 2 decades, causes remain unclear. The disease is characterized by progressive loss of kidney function with the absence of heavy proteinuria and hematuria. The International Prospective Study of CKD of Unknown Etiology in Agricultural Communities (CURE study) aims to do the following: (i) identify factors associated with kidney function decline among individuals with or at risk for CKD of uncertain etiology (CKDu); (ii) better characterize the clinical phenotypes of individuals with CKDu and differentiate them from other forms of CKD; (iii) employ advanced laboratory and data analysis methods to conduct discovery science related to risk factors, biomarkers, and causal mechanisms; and (iv) establish a biorepository for future research. Methods: , no evidence of diabetes, and no other known causes of CKD. Biological samples and questionnaire data are collected from participants during 4 visits at 8-month intervals. Results: Blood, urine, and hair will be analyzed for kidney function biomarkers, trace elements, pesticides and other contaminants, untargeted metabolomics, and genetic assays. Environmental samples, collected from a subset of study participants, will be analyzed for trace elements, agrochemicals, and burning exposures. Conclusion: This study will provide novel information about CKDu etiology and clinical phenotypes across distinct geographies.

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)0.001
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 score0.008
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.019
GPT teacher head0.292
Teacher spread0.272 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

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