Chronic kidney disease–mineral and bone disorder: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference
Bibliographic record
Abstract
In 2017, Kidney Disease: Improving Global Outcomes (KDIGO) published a Clinical Practice Guideline Update for the Diagnosis, Evaluation, Prevention, and Treatment of Chronic Kidney Disease-Mineral and Bone Disorder (CKD-MBD). Since then, new lines of evidence have been published related to evaluating disordered mineral metabolism and bone quality and turnover, identifying and inhibiting vascular calcification, targeting vitamin D levels, and regulating parathyroid hormone. For an in-depth consideration of the new insights, in October 2023, KDIGO held a Controversies Conference on CKD-MBD: Progress and Knowledge Gaps Toward Personalizing Care. Participants concluded that the recommendations in the 2017 CKD-MBD guideline remained largely consistent with the available evidence. However, the framework of the 2017 Guideline, with 3 major sections-biochemical abnormalities in mineral metabolism; bone disease; and vascular calcification-may no longer best reflect currently available evidence related to diagnosis and treatment. Instead, future guideline efforts could consider mineral homeostasis and deranged endocrine systems in adults within a context of 2 clinical syndromes: CKD-associated osteoporosis, encompassing increased fracture risk in patients with CKD; and CKD-associated cardiovascular disease, including vascular calcification and structural abnormalities, such as valvular calcification and left ventricular hypertrophy. Participants emphasized that the complexity of bone and cardiovascular manifestations of CKD-MBD necessitates personalized approaches to management.
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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 | 0.045 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".