Bibliographic record
Abstract
Since the first intravenous nutrition support attempt with olive oil in the 17th century, intravenous fat emulsions (IVFEs) have evolved to become an integral component in the management of patients receiving home parenteral nutrition (HPN). IVFEs serve as a calorie source and provide essential fatty acids (linoleic acid and α-linolenic acid) in patients unable to achieve adequate intake of these fatty acids through alternative means. However, IVFE use is also associated with multiple complications, including increased infection risk, liver disease, and systemic proinflammatory states. In the United States, most IVFEs are composed of 100% soybean oil; internationally multiple alternative IVFEs (using fish oil, olive oil, and long- and medium-chain triglycerides) are available or being developed. The hope is that these IVFEs will prevent, or decrease the risk of, some of the HPN-associated complications. The goal of this article is to review how IVFEs came into use, their composition and metabolism, options for IVFE delivery in HPN, benefits and risks of IVFE use, and strategies to minimize the risks associated with IVFE use in HPN patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".