Greater increase in urinary hepcidin predicts protection from acute kidney injury after cardiopulmonary bypass
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is a common and serious complication of cardiopulmonary bypass (CPB) surgery. Hepcidin, a peptide hormone that regulates iron homeostasis, is a potential biomarker of AKI following CPB. METHODS: We investigated the association between post-operative changes in serum and urinary hepcidin and AKI in 93 patients undergoing CPB. RESULTS: Twenty-five patients developed AKI based on the Risk, Injury, Failure, Loss, End-stage kidney disease (RIFLE) criteria in the first 5 days. Serum hepcidin, urine hepcidin concentration, the urinary hepcidin:creatinine ratio and fractional excretion of hepcidin in urine rose significantly after surgery. However, urine hepcidin concentration and urinary hepcidin:creatinine ratio were significantly lower at 24 h in patients with RIFLE-Risk, Injury or Failure compared to those without AKI (P = 0.0009 and P < 0.0001, respectively). Receiver operator characteristic analysis showed that lower 24-h urine hepcidin concentration and urinary hepcidin:creatinine ratio were sensitive and specific predictors of AKI. The urinary hepcidin:creatinine ratio had an area under the curve for the diagnosis of RIFLE ≥ risk at 24 h of 0.77 and of 0.84 for RIFLE ≥ injury. Urinary hepcidin had similar predictive accuracy. Such predictive ability remained when patients with early creatinine increases were excluded. CONCLUSIONS: Urinary hepcidin and hepcidin:creatinine ratio are biomarkers of AKI after CPB, with an inverse association between its increase at 24 h and risk of AKI in the first five post-operative days. Measuring hepcidin in the urine on the first day following surgery may deliver earlier diagnosis and interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".