Urine Abnormalities in Acute Kidney Injury and Sepsis
Bibliographic record
Abstract
Acute kidney injury (AKI) is a common complication of critical illness. While the etiology of AKI in critically ill patients is likely often multifactorial, sepsis has consistently been found an important contributing factor and has been associated with high attributable morbidity and mortality. Accordingly, the timely identification of septic AKI in critically ill patients is clearly a clinical priority. The diagnosis of AKI has traditionally depended upon biochemical measurements such as serum creatinine, urea, and urine output. In addition, several urinary biochemical tests, derived indices and microscopy have also been widely cited as valuable in the diagnosis and classification of AKI. However, the value of these urinary tests in the diagnosis, classification, prognosis and clinical management in septic AKI remains unclear, due in part to a lack of kidney morphologic changes and histopathology in human studies of septic AKI. This review will summarize the urinary biochemistry and microscopy in septic AKI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".