Essential Fatty Acid Requirements and Intravenous Lipid Emulsions
Bibliographic record
Abstract
Linoleic acid (LA) and α-linolenic acid (ALA) must be supplied to the human body and are therefore considered essential fatty acids. This narrative review discusses the signs, symptoms, diagnosis, prevention, and treatment of essential fatty acid deficiency (EFAD). EFAD may occur in patients with conditions that severely limit the intake, digestion, absorption, and/or metabolism of fat. EFAD may be prevented in patients requiring parenteral nutrition by inclusion of an intravenous lipid emulsion (ILE) as a source of LA and ALA. Early ILEs consisted solely of soybean oil (SO), a good source of LA and ALA, but being rich in LA may promote the production of proinflammatory fatty acids. Subsequent ILE formulations replaced part of the SO with other fat sources to decrease the amount of proinflammatory fatty acids. Although rare, EFAD is diagnosed by an elevated triene:tetraene (T:T) ratio, which reflects increased metabolism of oleic acid to Mead acid in the absence of adequate LA and ALA. Assays for measuring fatty acids have improved over the years, and therefore it is necessary to take into account the particular assay used and its reference range when determining if the T:T ratio indicates EFAD. In patients with a high degree of suspicion for EFAD, obtaining a fatty acid profile may provide additional useful information for making a diagnosis of EFAD. In patients receiving an ILE, the T:T ratio and fatty acid profile should be interpreted in light of the fatty acid composition of the ILE to ensure accurate diagnosis of EFAD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".