Chronic kidney disease–mineral and bone disorder: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Сonference
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 (CKDMBD). 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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.009 | 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".