Influence of Soybean Oil or Non-Soybean Oil Based Lipid Emulsions on Parenteral Nutrition Associated Liver Disease in Late Preterm and Term Infants
Bibliographic record
Abstract
Background: Total parenteral nutrition (TPN) is a life-saving therapy given to neonates with intestinal failure. However, infants on long-term TPN may experience Parenteral Nutrition-Associated Liver Disease (PNALD). New formulations for lipid emulsions are purportedly better than the traditional soy-based lipid emulsions (SLE). Our primary objective was to determine the prevalence of PNALD in infants who received non-soybean-based lipid emulsions (NSLE) or SLE. Methods: In this retrospective study, medical records of all infants admitted to a tertiary neonatal intensive care unitfrom 2004 to 2013 were reviewed. Late preterm (34 -36 weeks of gestation) and term infants who were on TPN for more than two weeks were included. Their demographic data and clinical variables were collected. Results: 208 infants received SLE for more than two weeks. The prevalence rate of PNALD in those who received SLE was 21% while that of those who received the NSLE was 17%. No significant difference was found between the ‘Soy’ or ‘NonSoy’ subgroups (p = 0.315). Seventy infants received TPN for more than four weeks. The prevalence rate of PNALD in infants who received SLE and NSLE was 35% and 25% respectively. No significant statistical difference was found between the ‘Soy’ or ‘NonSoy’ subgroups (p = 0.132). Conclusions: The type of lipid emulsion does not significantly influence the rate of PNALD in late preterm and term infants on long-term TPN.
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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 | 0.005 |
| 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".