New lipids in enteral feeding
Bibliographic record
Abstract
PURPOSE OF REVIEW: Lipid sources for enteral nutrition continue to be an exciting area of investigation. It is timely to review recent developments which have largely contributed to thrust enteral feeding into a new era. RECENT FINDINGS: Although much more research needs to be done, there is a better understanding of the competitive relationships between n-6/n-3 fatty acids in conditions of metabolic and immune stress as well as in autoimmune and degenerative diseases. Although structured lipids are more completely absorbed and cleared, other more important clinical benefits need to be documented before they can be considered cost-effective. Immune enhancing formulas are the subject of controversy and some have been shown to be more effective than others. Enteral formulations with short-chain fatty acids are promising but more experimental work on the normal, and the sick colon is needed. Finally, there are a few isolated studies suggesting that enteral feeding with liposomes and with lipolytic products may have advantages when the digestive phase needs to be circumvented. The era of nutrigenomics, in which the effect of specific lipids on genes and proteins is being explored, is with us. We can look forward to nutrigenetics when the effect of genetic variation on the interaction between diet and disease will guide our practice. SUMMARY: Clinicians already have access to lipid sources and formulations which allow them to individualize enteral feeding programs. More clinical and technological research needs to be carried out, however, before products can be tailored to produce optimal effects in specific conditions.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".