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 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 | 0.009 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".