#3493 Two decades of dialysis in 41 countries: the new MONitoring Dialysis Outcomes (MONDO) initiative dataset
Bibliographic record
Abstract
Abstract Background and Aims Data captured during dialysis care delivery can be used for monitoring outcomes yet is unable to distinguish the influences of local practice patterns versus pathophysiology. The MONitoring Dialysis Outcomes (MONDO) initiative is an academia-industry collaboration whereby providers can contribute data to an anonymized dataset used for joint research purposes. We present key demographics, cause of end-stage kidney disease (ESKD), and regional information to provide an understanding of patient characteristics in the new MONDO dataset. Method Data from Jan 2000–Dec 2019 on 366 fields was captured longitudinally on a per patient per observation level (e.g., each laboratory, treatment, value) with a universal data structure. Data anonymization was performed in alignment with recommendations from a re-identification risk determination (Privacy Analytics, Ontario, CA). The approach satisfies internal best practices, such as ISO/IEC 27559. Ethics approval for the MONDO dataset was obtained in the U.S., with data de-identified prior to analysis to ensure compliance with relevant guidelines. Final datasets were consolidated and hosted by the Renal Research Institute. Results The dataset includes information from 292,531 patients in 41 countries across 20 years. Data was contributed from nine distinct providers. Male represents 58.9% and the age distribution of dialysis patients worldwide showed most were aged 45–64 years (38.3%), followed by 65–74 years (23.6%), and ≥75 years (19.2%). Younger age groups, including 18–44 years (17.3%) and 0–17 years (1.5%), comprised a smaller proportion of the dataset. The distribution of race showed 42.5% were white race, 13.4% other race, 3.8% black Race, and 1.1% Asian race. A considerable proportion (39.2%) had unreported or missing race data. The primary cause of ESKD was most commonly unspecified/unknown (40.5%), followed by diabetes (15.8%), glomerular diseases (13.9%), and hypertensive diseases (14.0%). Less common causes include renal tubulo-interstitial diseases (5.6%), polycystic kidney disease (PKD) (3.7%), and cystic kidney disease other than PKD (0.37%). The geographic distribution of patients in the dataset has the highest representation from Latin America (33.4%), followed by Eastern Europe (23.3%), Southern Europe (14.3%), and Western Asia (8.5%). Other regions had lower yet meaningful representations, including Northern America (6.4%), Northern Europe (4.8%), Eastern Asia (0.5%), and Oceania (0.35%). Conclusion The MONDO initiative developed a new global research dataset to study dialysis outcomes, pathophysiology inference, and risk prediction. This dataset provides a comprehensive and internationally generalizable overview of ESKD patients, dialysis care, and outcomes across multiple countries and at multiple providers. The profiles of demographics and ESKD etiology act as benchmarks for the nephrology community and emphasize the importance of diabetes, hypertensive diseases, and glomerular disease management in kidney disease prevention. MONDO is open to collaboration and contributions without geographic restrictions. This dataset includes 20 years of longitudinal patient data and is among the most comprehensive global dialysis datasets available.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".