Use of Biomarkers to Assess Prognosis and Guide Management of Patients with Acute Kidney Injury
Bibliographic record
Abstract
Several new biomarkers of kidney damage have been characterized and are being validated in clinical studies. These damage biomarkers complement existing conventional biomarkers of kidney function (e.g. serum creatinine, serum urea, and urine output) that are currently utilized to diagnose and stage acute kidney injury (AKI). Both functional and damage biomarkers provide an opportunity to identify patients with AKI who are at risk for a less favorable prognosis in terms of worsening damage or further declines in kidney function and likelihood of need for renal replacement. We performed a systemic search and review of the available literature pre-conference. Our workgroup presented the findings in multiple rounds to the ADQI conference members and a final summary and review was refined in an iterative approach. The specific clinical situations of renal or liver transplantation, or cirrhosis/hepatorenal syndrome were not included. Overall, multiple AKI biomarkers have been well characterized for utilization for AKI prognosis. These functional and damage markers can be used to assist in decisions related to triage of patients with AKI and identifying patients with who are at risk for progression. Set cut-offs for various biomarkers and their bedside utility are forthcoming and will be in part determined by regulatory intended use guidelines, platform standardization, and inter-laboratory calibration. There remain many unresolved areas of AKI biomarker use in selected syndromes of AKI (e.g. cardiorenal syndrome, hepatorenal syndrome). As clinicians gain experience with AKI biomarkers, clinical care plans that incorporate them into routine care will shortly follow.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".