Bibliographic record
Abstract
PURPOSE OF REVIEW: Enteral nutrition is now widely used as the preferred route of nutritional support in malnourished and intensive care unit patients. Studies providing evidence for efficacy, techniques of administration, and outcome are appearing daily in the literature. This review presents evidenced-based studies in this field from December 2002 to the present and critically reviews them for the reader. In this way the reader can rapidly access important publication from the morass being published each year. RECENT FINDINGS: A diverse group of studies are covered in this review: effect of nutritional status on outcome, effect of combining enteral and parenteral nutrition, enteral nutrition in pancreatitis, rehydration of infants, gastric versus intestinal feeding, nutrition in hip fractures and pressure ulcers, systematic reviews and guidelines, immunonutrition and enterocolitis in infant feeding SUMMARY: Enteral nutrition is an established modality of nutritional support that has received wide acceptance. It is not clear, however, for which conditions it improves patient outcome and the best way to optimize its delivery. In this review, articles addressing the outcome of patients and methods to optimize delivery of enteral nutrition are reviewed. Unfortunately, with few exceptions, most studies are based on few patients or do not have a placebo arm. An more important flaw in these studies is the nutritional status of the patient and need for support.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".