Monitoring Dialysis Outcomes across the World - The MONDO Global Database Consortium
Bibliographic record
Abstract
BACKGROUND/AIMS: Dialysis providers frequently collect detailed longitudinal and standardized patient data, providing valuable registries of routine care. However, even large organizations are restricted to certain regions, limiting their ability to separate effects of local practice from the pathophysiology shared by most dialysis patients. To overcome this limitation, the MONDO (MONitoring Dialysis Outcomes) research consortium has created a platform for the joint analysis of data from almost 200,000 dialysis patients worldwide. METHODS: We examined design and operation of MONDO as well as its methodology with respect to patient inclusion, descriptive data and other study parameters. RESULTS: MONDO partners contribute primary databases of anonymized patient data and collaboratively analyze populations across national and regional boundaries. To that end, datasets from different electronic health record systems are converted into a uniform structure. Patients are enrolled without systematic exclusions into open cohorts representing the diversity of patients. A large number of patient level treatment and outcome data is recorded frequently and can be analyzed with little delay. Detailed variable definitions are used to determine if a parameter can be studied in a subset or all databases. CONCLUSION: MONDO has created a large repository of validated dialysis data, expanding the opportunities for outcome studies in dialysis patients. The density of longitudinal information facilitates in particular trend analysis. Limitations include the paucity of uniform definitions and standards regarding descriptive information (e.g. comorbidities), which limits the identification of patient subsets. Through its global outreach, depth, breadth and size, MONDO advances the observational study of dialysis patients and 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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| 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".