Harmonizing acute and chronic kidney disease definition and classification: report of a Kidney Disease: Improving Global Outcomes (KDIGO) Consensus Conference
Bibliographic record
Abstract
Kidney disease is an important public health problem. Both acute kidney injury (AKI) and chronic kidney disease have been well defined and classified, leading to improved research efforts and subsequent management strategies and recommendations. For those patients with abnormalities in kidney function and/or structure who meet neither the definition of AKI nor chronic kidney disease, there remains a gap in research, care, and guidance. The term acute kidney diseases and disorders, abbreviated to acute kidney disease (AKD), has been introduced as an important construct to address this. To expand and harmonize existing definitions and to ultimately better inform research and clinical care, Kidney Disease: Improving Global Outcomes (KDIGO) organized a consensus workshop. Multiple invitees from around the globe, representing both acute and chronic kidney disease researchers and experts, met virtually to examine existing data, and discuss key concepts related to AKD. Despite some remaining unresolved questions, conference attendees reached general consensus on the definition and classification of AKD, management strategies, and research priorities. AKD is defined by abnormalities of kidney function and/or structure with implications for health and with a duration of ≤3 months. AKD may include AKI, but, more importantly, also includes abnormalities in kidney function that are not as severe as AKI or that develop over a period of >7 days. The cause(s) of AKD should be sought, and classification includes functional and structural parameters. Management of AKD is currently based on empirical considerations. A robust research agenda to enable refinement and validation of definitions and classification systems, and thus testing of interventions and strategies, is proposed.
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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 | 0.082 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.013 | 0.009 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".