Parenteral lipid minimization versus composition for intestinal failure associated liver disease
Bibliographic record
Abstract
Introduction Strategies to treat intestinal failure associated liver disease (IFALD) manipulate dose or composition of parenteral lipids. We compared low dose omega‐6 (soybean oil), low dose omega‐3 (fish oil) and standard dose therapies using soybean or a combination of soybean, medium chain triglycerides, olive and fish oil, on liver and nutritional outcomes in neonates. Materials and Methods Neonatal pigletswere allocated to: Group 1 (n=6) Omegaven® at 5 g/kg/d, Group 2 (n=8) Intralipid® at 5 g/kg/d, Group 3 (n=9) Intralipid® at 10 g/kg/d, Group 4 piglets (n=10) SMOFlipid ® at 10g/kg/d and Group 5 sow‐reared controls (n=8). After 14 days of total parenteral nutrition we compared bilirubin, bile flow, weight and brain weight. Results by Group (mean/SD) 1 2 3 4 5 Total Bilirubin (microM/L) 7.0 a (5.3) 10.8 a (3.2) 21.1 b (10.7) 4.0 c (1.2) 8.2 a (3.9) Bile flow (mcg/g liver) 12.3 a (6.8) 7.9 a (3.8) 5.4 b (4.0) 13.5 a (6.6) 9.0 a (1.9) Total weight gain (kg) 2.7 a (0.2) 2.4 a (0.3) 2.2 a (0.5) 3.0 c (0.3) 3.9 b (0.9) Brain weight (g) 35.3 a (1.8) 36.3 a (3.3) 41.6 ab (4.6) 44.2 b (3.4) 45.9 b (3.4) Superscripts refer to differences by ANOVA p<0.05 Discussion Groups 1, 2 and 4 were equally effective in preventing IFALD. Based on weight gain and final brain weight SMOFlipid ® provides beneficial nutritional support to vulnerable neonates, while protecting from cholestasis. The source of research support is CIHR with Fresenius Kabi Grant Funding Source: CIHR
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".