The case for early identification and intervention of chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference
Bibliographic record
Abstract
Chronic kidney disease (CKD) causes substantial global morbidity and increases cardiovascular and all-cause mortality. Unlike other chronic diseases with established strategies for screening, there has been no consensus on whether health systems and governments should prioritize early identification and intervention for CKD. Guidelines on evaluating and managing early CKD are available but have not been universally adopted in the absence of incentives or quality measures for prioritizing CKD care. The burden of CKD falls disproportionately upon persons with lower socioeconomic status, who have a higher prevalence of CKD, limited access to treatment, and poorer outcomes. Therefore, identifying and treating CKD at the earliest stages is an equity imperative. In 2019, Kidney Disease: Improving Global Outcomes (KDIGO) held a controversies conference entitled "Early Identification and Intervention in CKD." Participants identified strategies for screening, risk stratification, and treatment for early CKD and the key health system and economic factors for implementing these processes. A consensus emerged that CKD screening coupled with risk stratification and treatment should be implemented immediately for high-risk persons and that this should ideally occur in primary or community care settings with tailoring to the local context.
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 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 | 0.075 | 0.106 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.040 | 0.072 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".