Gastrointestinal Biomarkers and Their Association with Feeding in the First Five Days of Pediatric Critical Illness
Bibliographic record
Abstract
OBJECTIVES: Predicting the patients' tolerance to enteral nutrition (EN) would help clinicians optimize individual nutritional intake. This study investigated the course of several gastrointestinal (GI) biomarkers and their association with EN advancement (ENA) longitudinally during pediatric intensive care unit (PICU) admission. METHODS: This is a secondary analysis of the Early versus Late Parenteral Nutrition in the Pediatric Intensive Care Unit randomized controlled trial. EN was started early and increased gradually. The cholecystokinin (CCK), leptin, glucagon, intestinal fatty acid-binding protein 2 (I-FABP2), and citrulline plasma concentrations were measured upon PICU admission, day 3 and day 5. ENA was defined as kcal EN provided as % of predicted resting energy expenditure. The course of the biomarkers and ENA was examined in patients with samples on all time points using Friedman and Wilcoxon signed-rank tests. The association of ENA with the biomarkers was examined using a 2-part mixed-effects model with data of the complete population, adjusted for possible confounders. RESULTS: For 172 patients, median age 8.6 years (first quartile; third quartile: 4.2; 13.4), samples were available, of which 55 had samples on all time points. The median ENA was 0 (0; 0) on admission, 14.5 (0.0; 43.8) on day 3, and 28.0 (7.6; 94.8) on day 5. During PICU stay, CCK and I-FABP2 concentrations decreased significantly, whereas glucagon concentrations increased significantly, and leptin and citrulline remained stable. None of the biomarkers was longitudinally associated with ENA. CONCLUSIONS: Based on the current evidence, CCK, leptin, glucagon, I-FABP2, and citrulline appear to have no added value in predicting ENA in the first 5 days of pediatric critical illness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".