The Role of Urinary Neutrophil Gelatinase-Associated Lipocalin in Predicting Acute Kidney Dysfunction in Patients With Liver Cirrhosis
Bibliographic record
Abstract
BACKGROUND: Early detection of acute kidney dysfunction (AKD) in cirrhotic patients is crucial. Urinary neutrophil gelatinase-associated lipocalin (uNGAL) has been identified as an early marker of AKD. The aim of the study was to evaluate serial uNGAL as a marker and predictor of AKD in liver cirrhosis patients. METHODS: Serial uNGAL and serum creatinine (sCr) levels were measured daily during the first 6 days of admission. Furthermore, sCr levels and the estimated glomerular filtration rate (eGFR) were measured after 3 - 6 weeks. The uNGAL levels in patients with and without abnormal sCr were compared. RESULTS: Fifty-seven consecutive cirrhotic patients were enrolled in the study. Eight of 14 patients (57%) who developed abnormal uNGAL level also had abnormal sCr level (odds ratio (OR) = 3.4, 95% CI: 0.99 - 12.03, P = 0.05). After 6 weeks, 41% of patients exhibited an abnormal uNGAL level and abnormal sCr (OR = 6.7, 95% CI: 1.55 - 28.85, P = 0.01). Area under the curve (AUROC) and the best cut-off point for highest NGAL in 6 days were 0.64 and 72.55 ng/mL, respectively. CONCLUSIONS: There is a modest association between highest uNGAL in the first 6 days of admission and sCr at week 6 in all participants. This may indicate that in cirrhotic patients, uNGAL level during the first 6 days of admission has a potential predictability for the development of high sCr and low eGFR 6 weeks later. The AUROC of 0.64 quantifies the overall ability of uNGAL to discriminate between those individuals who will have a raised sCr levels and those who will not.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".