Global data monitoring systems and early identification for kidney diseases
Bibliographic record
Abstract
BACKGROUND: Data monitoring and surveillance systems are the cornerstone for governance and regulation, planning, and policy development for chronic disease care. Our study aims to evaluate health systems capacity for data monitoring and surveillance for kidney care. METHODS: We leveraged data from the third iteration of the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA), an international survey of stakeholders (clinicians, policymakers and patient advocates) from 167 countries conducted between July and September 2022. ISN-GKHA contains data on availability and types of kidney registries, the spectrum of their coverage, as well as data on national policies for kidney disease identification. RESULTS: Overall, 167 countries responded to the survey, representing 97.4% of the global population. Information systems in forms of registries for dialysis care were available in 63% (n = 102/162) of countries, followed by kidney transplant registries (58%; n = 94/162), and registries for non-dialysis chronic kidney disease (19%; n = 31/162) and acute kidney injury (9%; n = 14/162). Participation in dialysis registries was mandatory in 57% (n = 58) of countries; however, in more than half of countries in Africa (58%; n = 7), Eastern and Central Europe (67%; n = 10), and South Asia (100%; n = 2), participation was voluntary. The least-reported performance measures in dialysis registries were hospitalization (36%; n = 37) and quality of life (24%; n = 24). CONCLUSIONS: The variability of health information systems and early identification systems for kidney disease across countries and world regions warrants a global framework for prioritizing the development of these systems.
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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 | 0.074 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".