Value of serum iron and urine neutrophil gelatinase-associated lipocalin in predicting the mortality of critically ill patients with sepsis
Bibliographic record
Abstract
INTRODUCTION: We aimed to investigate the association of iron metabolism-related parameters with 60-day mortality in critically ill patients with sepsis. METHODS: Serum or urine concentrations of iron metabolism-related parameters on intensive care unit admission were measured in a prospective cohort of 133 eligible patients with sepsis according to the Sepsis-3 criteria, and these values were compared between survivors and nonsurvivors, categorized according to their 60-day survival status. Cox regression analyses were performed to examine the association between iron parameters and 60-day mortality. Kaplan-Meier methods were used to illustrate the differences in survival between different iron parameters. RESULTS: Of the 133 patients included in the study, 61 (45.8%) had died by day 60. After adjusting for confounding variables, higher concentrations of serum iron (cut-off 9.5 μmol/mL) and higher concentrations of urine neutrophil gelatinase-associated lipocalin (uNGAL; cut-off 169.3 ng/mL) were associated with a significantly greater risk of death in the Cox regression analysis. These two biomarkers combined with Sequential Organ Failure Assessment (SOFA) scores increased the area under the receiver operating characteristic (AUROC) curve to 0.85. DISCUSSION: uNGAL: urine neutrophil gelatinase-associated lipocalin; ICU: intensive care unit; SOFA: Sequential Organ Failure Assessment; APACHE II: the Acute Physiology and Chronic Health Evaluation II; ELISA: enzyme-linked immunosorbent assay; HR: hazard ratio; CIs: confidence intervals; WBC: white blood cell; TBIL: total bilirubin.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".