THE CHRONIC KIDNEY DISEASE IN AFRICA (CKD-AFRICA) COLLABORATION: A NEW PAN-AFRICAN NETWORK
Bibliographic record
Abstract
Objective: Evidence points to an increasing prevalence of chronic kidney disease (CKD) across Africa, particularly in high-risk groups, like those with hypertension, diabetes, and HIV. However, there has not been a concerted, Africa-wide, effort to provide estimates to inform health services planning and policy development to address CKD. The CKD-Africa Collaboration seeks to fill this gap by collating data, at individual participant data (IPD) level, from African studies. The main aims of this platform are, (1) to utilize the available data from relevant prevalence studies of CKD, to provide updated and comprehensive syntheses on the burden of CKD in Africa, and (2) to link investigators in the field of CKD epidemiology and prevention, by providing a platform to plan future observational and interventional studies on CKD in Africa. Design and method: Establishing the CKD-Africa Collaboration followed a stepwise approach, through the identification of data sources, developing the database platform, and acquiring and processing participant-level data. Inclusion criteria: 1) observational study with primary data; 2) ethical approval; 3) minimum sample size of 300 participants and 4) participants of African descent residing in Africa. Results: To date, we have curated data from 42 studies conducted in 12 African countries, with a total of 37,842 participants (Fig.). The sample sizes ranges between 300 and 2,543 per study, with adults aged 18 to 100 years. In 36% of the studies data collection took place before 2010, with the remaining 64% sampled between 2010 and 2017. Overall, 74.6% of the IPD were sampled from the general population, with CKD prevalence estimates ranging from 1% to 22%. The remaining IPD were from high-risk populations, including people with hypertension, diabetes, HIV, and first-degree relatives of people with CKD. All studies used serum creatinine to estimate glomerular filtration rate, with 85.7% of these studies employing the Jaffe method. fx155 Conclusions: This network has far-reaching potential for Africa, as it is in an ideal position to test hypotheses and compare findings across geographical and national boundaries, and to generate a new understanding of CKD progression and its complications in this under-researched context.
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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 | 0.064 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".