Understanding the Need for Tools and Resources to Manage Enteral Nutrition Intolerance: An On-line Survey
Bibliographic record
Abstract
Purpose: Enteral nutrition intolerance (ENI) is a common complication among tube-fed patients, associated with reduced volumes of nutrition delivered, and may contribute to malnutrition risk. This research aimed to obtain insights about dietitians’ needs and preferences related to tools and resources to help identify and manage ENI. Methods: An online survey was administered to registered dietitians (RD) engaged in enteral nutrition (EN) management, recruited from a list of attendees at a national webinar. The 16-question survey asked about participant’s experience with ENI and interest in resources to manage ENI. Results: Of the 219 surveys completed (25% response rate), 86% identified ENI as an issue/concern that interferes with adequate nutrition or hydration for their patients. Ninety-seven percent reported being interested in having tools/resources to manage ENI. The symptoms identified as most pressing to manage were diarrhea (73%), bloating/abdominal discomfort (42%), and nausea (32%). Preferred types of tools were hard-copy resources (70%), algorithms (67%), and web-based instruments (62%). Conclusions: ENI remains an issue for clinicians working with tube-fed patients and RDs are interested in management tools. These results have implications for the development of evidence-based resources to help improve EN delivery and ultimately may contribute to clinician’s efforts at reducing malnutrition.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".