Primary Cause of Kidney Failure in 40 Countries in Apollo Dial DB
Bibliographic record
Abstract
Background: Diabetes is a main cause of kidney failure, but the variability in rates of disease etiology is unknown. We used a global dialysis database, Apollo Dial DB, to assess the primary cause of kidney failure in 40 countries. Methods: Apollo Dial DB is an anonymized database constructed from real-world data at a kidney care network (Fresenius Medical Care, Bad Homburg, DE). Longitudinal data is captured on an observation-level (each treatment, lab, value). First version has >360 variables (demographics, dialysis, labs, medications, surveys, & outcomes) from 01Jan2018-31Mar2021. Data anonymization was performed in alignment with recommendations from a re-identification risk determination (Privacy Analytics, Ontario, CA). We analyzed the primary cause of kidney failure defined as due to diabetes, other causes, or unspecified/unknown causes. Results: In 543,169 adults (age ≥18 years) treated by dialysis, 455,769 (80.6%) had a known primary kidney failure cause. Globally, kidney failure due to diabetes was 39.8%, other causes were 40.8%, and unspecified/unknown cause was 19.4%). Cause of primary kidney failure varied dramatically by country ranging for: diabetes from <1% to 49%, other causes from 1% to 77%, and unspecified/unknown cause from 4% to 98% (Figure 1). Conclusion: Leading primary cause of kidney failure is diabetes in four countries, other causes in 13 countries, and unspecified/unknown in 23 countries. Diabetic nephropathy is meaningful in select countries, but considering a global landscape, needs to be determined. The importance of other causes of kidney disease, such as hypertensive and glomerular disorders (including those in diabetes), need to be considered at a local level. The cause of kidney failure appears undetermined in many parts of the world. Findings bring to light the potential role of underdiagnosis, as well as diseases of unknown origin. Funding: Commercial Support - Fresenius Medical Care
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".