Guidelines for Classification of Acute Kidney Diseases and Disorders
Bibliographic record
Abstract
Recent efforts have standardized definitions and classification systems for acute kidney injury (AKI) and chronic kidney disease (CKD). These efforts have enhanced communication, recognition, and awareness of acute and CKDs and stimulated research on both disorders. However, abnormalities of kidney function and structure can occur that do not meet the current criteria for either disorder. Recognizing the need for a uniform approach encompassing both acute and chronic abnormalities of kidney function and structure, the Kidney Disease Improving Global Outcomes 2012 Clinical Practice Guideline for AKI Guidelines proposed an operational definition for acute kidney diseases and disorders (AKD) that encompasses both AKI and any newly recognized kidney disease that does not meet the current definitions for AKI or CKD. Recent commentaries have highlighted that it may be premature to adopt these criteria into clinical practice, but that they may be useful for application in epidemiologic studies. Future research is needed to better understand the clinical characteristics, incidence, and prognosis of AKD, as well as the implications of case identification based on the AKD criteria.
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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 | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.021 | 0.023 |
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".