Human Heredity and Health (H3) in Africa Kidney Disease Research Network
Bibliographic record
Abstract
CKD affects an estimated 14% of adults in sub-Saharan Africa, but very little research has been done on the cause, progression, and prevention of CKD there. As part of the Human Heredity and Health in Africa (H3Africa) Consortium, the H3Africa Kidney Disease Research Network was established to study prevalent forms of kidney disease in sub-Saharan Africa and increase the capacity for genetics and genomics research. The study is performing comprehensive phenotypic characterization and analyzing environmental and genetic factors from nine clinical centers in four African countries (Ghana, Nigeria, Ethiopia, and Kenya) over a 5-year period. Approximately 4000 participants with specified kidney disease diagnoses and 4000 control participants will be enrolled in the four African countries. In addition, approximately 50 families with hereditary glomerular disease will be enrolled. The study includes both pediatric and adult participants age <1 to 74 years across a broad spectrum of kidney diseases secondary to hypertension-attributed nephropathy, diabetes, HIV infection, sickle cell disease, biopsy-proven glomerular disease, and CKD of unknown origin. Clinical and demographic data with biospecimens are collected to assess clinical, biochemical, and genetic markers of kidney disease. As of March 2015, a total of 3499 patients and controls have been recruited and 1897 had complete entry data for analysis. Slightly more than half (50.2%) of the cohort is female. Initial quality control of clinical data collection and of biosample and DNA analysis is satisfactory, demonstrating that a clinical research infrastructure can be successfully established in Africa. This study will provide clinical, biochemical, and genotypic data that will greatly increase the understanding of CKD in sub-Saharan Africa.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".