Characteristics of Global Dialysis Data from Multiple Providers in the New MONitoring Dialysis Outcomes (MONDO) Dataset
Bibliographic record
Abstract
Background: 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 the characteristics of 20 years of longitudinal patient data contributed to the new MONDO dataset, which is now the most robust global dialysis dataset in the world. Methods: Data from Jan 2000-Dec 2019 on 382 fields was captured longitudinally on a per patient per observation level (e.g., each lab, treatment, value) with a universal data structure. Detailed distribution statistics were used for re-identification risk assessment and confirmed the anonymization logic for providers to apply and provide acceptably low risk, consistent with international data privacy standards (e.g., GDPR, HIPAA). Final datasets were consolidated and hosted by the Renal Research Institute. Results: Nine providers from 41 countries across six continents contributed to MONDO (289,715 patients across 20 years). Hemodialysis was the most used modality globally (Table 1). In Europe, Middle East & Africa, 63.2% of patients used hemodiafiltration (HDF) for ≥1 treatment. Peritoneal dialysis (PD) was used in 20% of patients in Latin America (LA) (20%) and 15% in North America (NA), yet ambulatory PD was more frequent in LA, while cycling PD was more frequent in NA. 11% of patients received a transplant. 42.5% died or stopped dialysis with some regional variability. Table 1. - Characteristics of patients in the MONDO dataset APAC EMEA LATAM NORTH AM Patient n 100.0% 100.0% (138940) 100.0% (98178) 100.0% 18735) Female 42.3% (14320) 40.0% (55599) 42.2% (41475) 41.9% (7844) HD 89.6% (30339) 78.1% (108493) 87.3% (85741) 93.7% (17552) HDF 31.2% (10556) 63.2% (87800) 10.0% (9810) 0.5% (91) Mixed HD/HDF 0.4% (143) 0.5% (687) <0.1% (49) N/A CAPD 0.2% (77) 2.7% (3768) 12.3% (12068) 6.3% (1181) CCPD <0.1% (8) 0.6% (884) 7.2% (7103) 8.7% (1623) Kidney function recovered 2.1% (728) 3.1% (4239) 5.7% (5588) 4.1% (760) Transplant 6.9% (2338) 13.1% (18192) 9.8% (9637) 10.0% (1880) Died or Withdrawal 30.6% (10356) 42.4% (58938) 48.6% (47705) 33.4% (6250) Data are shown as %(n) Modality data shows proportion and number of patients using the modality one or more times during the 20 years of follow up, Outcomes are based on crude counts that do not consider follow-up time. HD: hemadialysis; HDF: hemodiafiltration; CAPD: continuous ambulatory peritaneal dialysis; CCPD: continuous cyclic peritoneal dialysis APAC: Asia Pacific; EMEA: Europe. Middle East. Africa; LATAM: Latin America; NORTH AM; North America. Conclusions: MONDO developed a new global research dataset to study dialysis outcomes, pathophysiology, and risk prediction. MONDO is open to collaboration and contributions without geographic restrictions. 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".