Etiology of Kidney Failure across the World in MONitoring Dialysis Outcomes (MONDO) Dataset
Bibliographic record
Abstract
Background: MONDO initiative is an academic-industry partnership where providers contribute anonymized data to a shared research dataset. We characterized etiology of kidney failure (KF) from the MONDO2019 cohort from 41 countries over 20 years. Methods: Nine institutions contributed longitudinal data to MONDO 2019 (Jan2000-Dec2019). Data anonymization was performed in alignment with recommendations from a re-identification risk determination (Privacy Analytics, Ontario, CA). Results: MONDO 2019 represents 289,531 patients, of which 170,910 (59.0%) had a known etiology for KF (Figure 1). Worldwide, diabetes (DM) was the leading cause of KF (15.8%), followed by hypertensive diseases (HTD, 14.0%) and glomerular diseases (GD,13.9%). Although DM was the leading cause of KF in North America (NA, 35.1%) and Asia-Pacific (AP, 24.2%), GD was the most common cause of KF in Europe-Middle East-Africa (EMEA, 14.9%) and South America (SA, 16.5%). DM was the second leading cause of KF in EMEA (13.4%) and the third leading cause of KF in SA (12.7%). Other regional differences were observed and included HTD being a more common cause of KF in NA (25.3%) and AP (19.4%) than in SA (14.9%) and EMEA (10.6%). Conclusion: MONDO 2019 dataset shows that the etiology of KF may vary among world regions and warrants further confirmatory investigations, particularly for glomerular disorders associated with DM. These observations are consistent with findings from registries in US and Europe (Stel VS, et al., NDT 2024). 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".