Rationale and Design of the International Prospective Study of CKD of Uncertain Etiology in Agricultural Communities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".