Growing use of home enteral nutrition: a great tool in nutrition practice toolbox
Bibliographic record
Abstract
PURPOSE OF REVIEW: Home enteral nutrition (HEN) is a well-established practical nutrition therapy tool that is typically managed by an interdisciplinary team. Prevalence of HEN is increasing across the globe given significant evidence for utility, feasibility, efficacy, safety, and reliability of HEN in helping patients meeting their nutrition needs. The current review highlights the growing use of HEN in the context of what is novel in the field including trends in HEN practice with regards to tubes and connectors, feeding formula and real food blends, and common complications. The review also highlights that the use of HEN is expected to expand further over coming years emphasizing the need for national consensus recommendations and guidelines for HEN management. RECENT FINDINGS: The growing use of HEN has always been parallel to adoption of holistic definitions and concept of malnutrition in clinical nutrition practice and more understanding of the need for malnutrition risk stratification, meeting unmet needs in practice and addressing challenges that lead to suboptimal enteral nutrition. SUMMARY: Research and advancements in technology as well as in tube feeding formula industry have led to the development of more solutions and have helped identify and implement best HEN practices.
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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 | 0.003 | 0.013 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".