Trends and perspectives for improving quality of chronic kidney disease care: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference
Bibliographic record
Abstract
Chronic kidney disease (CKD) affects over 850 million people globally, and the need to prevent its development and progression is urgent. During the past decade, new perspectives have arisen related to the quality and precision of care for CKD, owing to the development of new tools and interventions for CKD diagnosis and management. New biomarkers, imaging methods, artificial intelligence techniques, and approaches to organizing and delivering healthcare may help clinicians recognize CKD, determine its etiology, assess the dominant mechanisms at given time points, and identify patients at high risk for progression or related events. As opportunities to apply the concepts of precision medicine for CKD identification and management continue to be developed, an ongoing discussion of the potential implications for care delivery is required. The 2022 KDIGO Controversies Conference on Improving CKD Quality of Care: Trends and Perspectives examined and discussed best practices for improving the precision of CKD diagnosis and prognosis, managing the complications of CKD, enhancing the safety of care, and maximizing patient quality of life. Existing tools and interventions currently available for the diagnosis and treatment of CKD were identified, with discussion of current barriers to their implementation and strategies for improving the quality of care delivered for CKD. Key knowledge gaps and areas for research were also identified.
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.
How this classification was reachedexpand
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".