The MONitoring Dialysis Outcomes (MONDO) Initiative
Bibliographic record
Abstract
BACKGROUND: Systematic collection and analysis of global hemodialysis patient data may help to improve patient outcomes. METHODS: The MONitoring Dialysis Outcomes (MONDO) initiative comprises data from eight dialysis providers worldwide. Data are combined into one repository. Extensive procedures are employed to merge data across countries and providers. RESULTS: The MONDO database comprises longitudinal data of currently 128,000 hemodialysis patients from 26 countries on five continents. Here we report data from 62,345 incident hemodialysis patients. We found lower catheter rates in South-East Asia and Australia, lower hemoglobin levels in South-East Asia, and a higher prevalence of diabetes in North America. Longitudinal analyses suggest that there is a decline in interdialytic weight gain and serum phosphorus and an increasing neutrophil-to-lymphocyte ratio before death in all regions studied. CONCLUSIONS: While organizationally lean and low-cost, MONDO is the largest global dialysis database initiative to date, with a particular focus on high longitudinal data density and geographical diversity.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".