Olive Oil and Soybean Oil Based Intravenous Lipid Emulsions, Liver Biochemistry and Clinical Outcomes
Bibliographic record
Abstract
Intravenous lipid emulsions are an essential component of parenteral nutrition (PN). Omega-6 reducing strategies may improve outcomes, including reduced PN associated liver disease, however evidence to support this recommendation is insufficient. The primary objective was to compare serum alkaline phosphatase (ALP), among patients provided with either soybean oil (Intralipid) or predominantly olive oil (Clinoleic) lipid emulsions. In this quasi-experimental study, we reviewed the medical records of surgical and medical adult patients who received lipid emulsions for at least seven consecutive days. Among the 206 patients (110—Intralipid, 96—Clinoleic) there was no significant difference in ALP and remaining liver function tests within 2 weeks of PN therapy initiation between groups, even after control for lipid doses. Macronutrient dosing was similar. Triglyceride level was higher by 0.7 mmol/L in the Clinoleic group; confidence interval 0.21 to 1.1; p = 0.004. The 30-day mortality, length of hospital stay, and proportion of patients admitted to intensive care were not significantly different. The Clinoleic group had a higher infection rate (36% vs. 22%, p = 0.031) and longer intensive care stays (p = 0.045). Well-designed randomized clinical trials comparing these lipid emulsions are necessary to confirm Intralipid superiority over Clinoleic in relation to infections and serum triglycerides.
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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 | 0.003 |
| 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 | 0.000 | 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".