A New Initiative in Nephrology: ‘Kidney Disease: Improving Global Outcomes’
Bibliographic record
Abstract
The burden of kidney disease: Improving global outcomes. Chronic kidney disease (CKD) is a worldwide public health problem with an increasing incidence and prevalence of patients requiring replacement therapy. There is an even higher prevalence of patients in earlier stages of CKD, with adverse outcomes such as kidney failure, cardiovascular disease, and premature death. Patients at earlier stages of CKD can be detected through laboratory testing and their treatment is effective in slowing the progression to kidney failure and reducing cardiovascular events. The evidence-based care of these patients are universal and independent of their geographic location. This paper describes the need to develop a uniform and global public health approach to the worldwide epidemic of CKD. It is to this end that a new initiative Kidney Disease: Improving Global Outcomes' has been established. Some current and future activities of this initiative are described. They include among others modification of the classification of CKD, the development of guidelines on hepatitis C, the organisation of consensus conferences like on Renal Osteodystrophy, and the creation of a website allowing the comparison of the five main English language clinical practice guidelines in kidney disease worldwide.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".