Blenderized Enteral Nutrition Diet Study: Feasibility, Clinical, and Microbiome Outcomes of Providing Blenderized Feeds Through a Gastric Tube in a Medically Complex Pediatric Population
Bibliographic record
Abstract
BACKGROUND: Chronically ill children often require feeding via gastrostomy tubes (G-tubes). Commercial formula is most commonly used for enteral feeding; however, caregivers have been requesting blenderized tube feeds (BTFs) as an alternative. The objective of this study was to evaluate the feasibility of using BTFs in a medically complex pediatric population and assess their impact on clinical outcomes, as well as the microbiota. METHODS: Twenty pediatric participants were included. Participants were G-tube dependent and receiving ≥75% of their daily energy requirements from commercial formula. Over 4 weeks, participants were transitioned from commercial formula to BTF and were monitored for 6 months for changes in nutrient intake, gastrointestinal symptoms, oral feeding, medication use, and caregiver perceptions. Changes to intestinal microbiota were monitored by 16S rDNA-based sequencing. RESULTS: Transition onto BTF was feasible in 17 participants, and 1 participant transitioned to oral feeds. Participants required 50% more calories to maintain their body mass index while on BTFs compared with commercial formula. BTF micronutrient content was superior to commercial formula. Prevalence of vomiting and use of acid-suppressive agents significantly decreased on BTFs. Stool consistency and frequency remained unchanged, while stool softener use increased. The bacterial diversity and richness in stool samples significantly increased, while the relative abundance of Proteobacteria decreased. Caregivers were more satisfied with BTFs and unanimously indicated they would recommend BTFs. CONCLUSION: Initiation and maintenance of BTFs is not only feasible in a medically complex pediatric population but can also be associated with improved clinical outcomes and increased intestinal bacterial diversity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".