Feeding May Modulate the Relationship Between Systemic Inflammation, Insulin Resistance, and Poor Outcome Following Cardiopulmonary Bypass for Pediatric Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Hyperglycemia is common following cardiopulmonary bypass (CPB) surgery and is associated with poor outcomes, often attributed to hyperinsulinemia and an acquired state of insulin resistance. This study examined the underpinnings of hyperglycemia and the effects of nutrition on the association with inflammation and clinical outcomes. METHODS: This prospective, observational cohort study enrolled consecutive children (<18 years) undergoing CPB. Serial measurements of inflammatory cytokines, glucose, insulin, and nutrition delivery were obtained. Glucose-insulin ratio (G:I) was calculated for each time point as a measure of insulin resistance (lower G:I reflects higher resistance). Clinical outcomes were recorded using a composite morbidity score. RESULTS: The 200 subjects studied were predominantly females (58%) undergoing biventricular repair (85%) at a median (interquartile range) age of 0.58 years (0.28, 3.4) and weight of 7.0 kg (3.1, 59.5). Hyperglycemia was common (49% of patients), coinciding with peak cytokine concentrations. Insulin levels were highest and G:I lowest immediately following separation from CPB but had no consistent relationship with cytokines. The morbidity outcome was reached by 23% of patients, with increased odds associated with higher interleukin (IL)6 and IL8 levels but not by glucose, insulin, or G:I. Providing higher feeding volumes attenuated this association between inflammation and morbidity. Higher feeds were not associated with G:I but appeared to decrease the strength of the relationship between cytokines and glycemic indices. CONCLUSION: Postoperative morbidity is independently associated with increased inflammation but not with hyperglycemia or markers of insulin resistance. Higher feeding volume may modify these relationships and have a protective role.
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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 | 0.002 |
| 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 | 0.001 | 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".