Dietitian's approach to managing enteral nutrition intolerance when a formula change is indicated: A clinical practice survey
Bibliographic record
Abstract
BACKGROUND: Enteral nutrition intolerance (ENI) is often defined as one or more gastrointestinal (GI) symptoms related to enteral nutrition (EN) and may have significant impact on patient outcomes. There are multiple strategies to help manage ENI, such as changing the EN formula. The objective of this practice survey was to understand prevalence of ENI, management of ENI symptoms, and EN formula features considered when changing formulas to manage ENI. METHODS: Canadian clinical dietitians working across care settings (n = 4827) were invited to complete a 28-question online survey if involved in the management of adult and/or pediatric patients receiving EN. RESULTS: Five hundred seventeen surveys were analyzed. Significantly more dietitians in adult vs pediatric settings (83.4% and 59.1%, respectively; P = 0.0012), reported ENI in <40% of patients. Assessing medications, elevating the head of the bed, and changing EN infusion rate, volume, or feeding regimen were the highest-ranked strategies to manage ENI symptoms. Most (>90%) respondents change the EN formula <50% of the time to manage ENI. Dietitians consider caloric density and protein form as the most important EN features to manage upper-GI symptoms vs fiber source, osmolality, and form of protein to manage lower-GI symptoms. EN with real-food ingredients was ranked higher in importance for managing upper- and lower-GI symptoms by dietitians in pediatric vs adult settings. CONCLUSION: To manage ENI symptoms, dietitians consider multiple strategies before deciding to change the EN formula. When a formula change is indicated, dietitians consider different EN features for the management of upper- and lower-GI symptoms.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".