Use of Biomarkers to Assess Prognosis and Guide Management of Patients with Acute Kidney Injury
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it