Understanding the use and tolerance of a pediatric and an adult commercial blenderized enteral formula through real‐world data
Bibliographic record
Abstract
BACKGROUND: Home enteral nutrition (HEN) is frequently prescribed to individuals who cannot consume adequate food orally. Commercial blenderized enteral formulas (CBEF) containing real-food ingredients are becoming more popular and more widely available; however, the demographics of patients receiving these formulas have rarely been evaluated, and little data are available on patient tolerance in the community. METHODS: US claims data were obtained for children and adolescents/adults who used the CBEF of interest as the sole source of nutrition via enteral feeding tube in the community setting following discharge from acute care. Demographics, concomitant medications, clinical diagnoses, and Charlson Comorbidity Index scores were tabulated using descriptive statistics. Gastrointestinal (GI) symptoms before and after hospital discharge were compared using significance tests. RESULTS: The study included 231 participants (180 children, 51 adolescents/adults). CBEFs were prescribed to patients with a variety of diagnoses, of which the most common were digestive and respiratory disorders. Children experienced significantly lower rates of diarrhea, nausea, vomiting, constipation, and abdominal distension in the weeks following hospital discharge compared with the baseline (all P < 0.001). Adolescents/adults experienced significantly lower rates of constipation, nausea, and vomiting (all P < 0.05). Neither group increased their usage of GI medications following hospital discharge. CONCLUSION: These CBEFs, based on real-food ingredients, were prescribed to diverse patients in the community and were well tolerated. These formulas offer an alternative to standard polymeric formulas and an alternative or adjunct to homemade blenderized formulas.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".