The Clinical Utility of Kidney Injury Molecule 1 in the Prediction, Diagnosis and Prognosis of Acute Kidney Injury: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: This systematic review evaluates the clinical utility of a novel biomarker kidney injury molecule 1 (Kim-1) in the prediction, diagnosis and prognosis of acute kidney injury (AKI). METHODS: We searched literature in electronic databases from January 2002 to December 2009 by the key words "kidney injury molecule 1" or "Kim-1" and "acute kidney injury" or "acute renal failure". Studies were eligible for inclusion if they were primary studies published in English, in which Kim-1 was measured for the purpose of prediction, diagnosis or prognosis of AKI in patients. RESULTS: Eight articles met the selection criteria for inclusion in the study. Compared to non AKI patients, Kim-1 increased significantly (at least p<0.05) in AKI patients by 2 hours after cardiac surgery. In the prediction of AKI in patients within 24 hours of cardiac surgery, the sensitivity of Kim-1 ranged from 92% to 100% and AUC between 0.78 and 0.91. Kim-1 increased significantly (at least p<0.05) in AKI established patients, especially in patients with acute tubular necrosis (ATN). The AUC of Kim-1 in the diagnosis of AKI was from 0.9 to 0.95. However, Kim-1 showed weak association with the need of renal replacement therapy and death of AKI patient. CONCLUSIONS: Kim-1 is a potential novel urinary biomarker in the early detection of AKI within 24 hours after kidney insult. It might be especially beneficial in the diagnosis of ischemic ATN.
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How this classification was reachedexpand
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".