Bibliographic record
Abstract
PURPOSE OF REVIEW: Intravenous lipid emulsions (IVLEs) are an essential component of parenteral nutrition. With the recent incorporation of new lipid emulsions into the Canadian and American market, the clinician responsible for prescribing these lipids should be educated regarding the different fatty acid (FA) profiles of these lipids, as well as their metabolic and functional effects. RECENT FINDINGS: New IVLEs contain a mix of soybean oil and olive oil, or a mix of soybean oil, coconut oil, olive oil and fish oil. These new lipid emulsions provide less essential fatty acids (FAs) (linoleic and alpha linolenic acids) than in pure soybean oil, yet incorporation of fish oil into an IVLE may decrease the amount of essential FAs required. Fish oil is a treatment for hypertriglyceridemia, and therefore, IVLEs that include fish oil may decrease serum triglycerides. Historical perspective is that fish oil can be associated with increased bleeding time. Evidence suggests that there is no association between fish oil and increased bleeding in patients, even those who are using anticoagulants. New IVLEs provide less vitamin K than soybean oil alone. Patients, or the parenteral nutrition solutions that include these new IVLEs should be supplemented with vitamin K. SUMMARY: Canadian and American Guidelines for IVLEs were based on soybean oil. Current practice should be tailored to which IVLE is being prescribed.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".