The Role of Parenteral Lipids in the Development of Advanced Intestinal Failure–Associated Liver Disease in Infants
Bibliographic record
Abstract
BACKGROUND: Given the recent interest in the role of ω-6 lipids in the development of intestinal failure-associated liver disease (IFALD), the authors sought to examine the role of parenteral lipids in the development of a serum conjugated bilirubin >100 µmol/L (5.9 mg/dL; CB100) in infants. METHOD: Between 2003 and 2004, data were collected prospectively on infants undergoing an abdominal surgical procedure. Univariate logistic regression models for the prediction of CB100 by 1 year postoperatively were developed. Predictors significant at the 0.2 level on univariate analysis were entered into a backward stepwise multiple variable logistic regression. RESULTS: Of 152 infants who received parenteral nutrition (PN) postoperatively, 22 developed CB100. Predictors that met criteria for consideration in the multiple-variable model were age, weight, small bowel length, presence of a stoma, proportion of enteral feeds postoperatively, septic episodes, days of maximal PN amino acid (>2.5 g/kg/d), days of maximal lipid (>2.5 g/kg/d), and PN duration. The final model included septic episodes (odds ratio, 3.23; 95% confidence interval, 1.8-5.9) and days of lipid >2.5 g/kg/d (1.04; 1.003-1.06). At 60 days of maximal lipid, the odds of advanced IFALD were increased 10-fold. CONCLUSIONS: This model suggests a key role of parenteral lipids and septic events in the development of CB100 from IFALD. These data may provide targets, such as careful line care, reduction in maximal lipid dose, or alternate lipids such as ω-3 fatty acids, to prevent CB100, an identified marker of subsequent liver failure from IFALD.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".