Neutrophil Gelatinase-Associated Lipocalin: Ready for Routine Clinical Use? An International Perspective
Bibliographic record
Abstract
Acute kidney injury (AKI) remains a challenge in terms of diagnosis and classification, its morbidity and mortality remaining high in the face of improving clinical protocols. Current clinical criteria use serum creatinine (sCr) and urine output to classify patients. Ongoing research has identified novel biomarkers that may improve the speed and accuracy of patient evaluation and prognostication, yet the route from basic science to clinical practice remains poorly paved. International evidence supporting the use of plasma neutrophil gelatinase-associated lipocalin (NGAL) as a valuable biomarker of AKI and chronic kidney disease (CKD) for a number of clinical scenarios was presented at the 31st International Vicenza Course on Critical Care Nephrology, and these data are detailed in this review. NGAL was shown to be highly useful alongside sCr, urinary output, and other biomarkers in assessing kidney injury; in patient stratification and continuous renal replacement therapy (CRRT) selection in paediatric AKI; in assessing kidney injury in conjunction with sCr in sepsis; in guiding resuscitation protocols in conjunction with brain natriuretic peptide in burn patients; as an early biomarker of delayed graft function and calcineurin inhibitor nephrotoxicity in kidney transplantation from extended criteria donors; as a biomarker of cardiovascular disease and heart failure, and in guiding CRRT selection in the intensive care unit and emergency department. While some applications require further clarification by way of larger randomised controlled trials, NGAL nevertheless demonstrates promise as an independent biological marker with the potential to improve earlier diagnosis and better assessment of risk groups in AKI and CKD. This is a critical element in formulating quick and accurate decisions for individual patients, both in acute scenarios and in long-term care, in order to improve patient prognostics and outcomes.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".