Acute Kidney Injury: Classification and Staging
Bibliographic record
Abstract
It was not until recently that consensus definitions for acute kidney injury (AKI) were proposed and published. The RIFLE (Risk-Injury-Failure-Loss-End-stage renal disease) and AKIN (Acute Kidney Injury Network) classifications were designed in order to be easily understood and applied in a variety of clinical and research settings. Their creation was intended to uniformly establish the presence or absence of the AKI and to give a quantitative idea of the severity of the disease unifying the commonly used parameters of serum creatinine and urine output. Subsequent validation showed that both the presence and severity of AKI, defined using RIFLE/AKIN, correlate well with patient outcome. This review will briefly describe the RIFLE/AKIN consensus definitions, its subsequent revisions and its successful validation and application to clinical research. The potential of extending the use of RIFLE/AKIN to the clinical setting of cardiorenal syndromes is also discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".