Monitoring Dialysis Outcomes across the World - The MONDO Global Database Consortium
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it