Hypoallergenic and anti-inflammatory feeds in children with complicated severe acute malnutrition: an open randomised controlled 3-arm intervention trial in Malawi
Bibliographic record
Abstract
Abstract Intestinal pathology in children with complicated severe acute malnutrition (SAM) persists despite standard management. Given the similarity with intestinal pathology in non-IgE mediated gastrointestinal food allergy and Crohn’s disease, we tested whether therapeutic feeds effective in treating these conditions may benefit children with complicated SAM. After initial clinical stabilisation, 95 children aged 6–23 months admitted at Queen Elizabeth Central Hospital, Blantyre, Malawi between January 1st and December 31st, 2016 were allocated randomly to either standard feeds, an elemental feed or a polymeric feed for 14 days. Change in faecal calprotectin as a marker of intestinal inflammation and the primary outcome was similar in each arm: elemental vs. standard 4.1 μg/mg stool/day (95% CI, −29.9, 38.15; P = 0.81) and polymeric vs. standard 10 (−23.96, 43.91; P = 0.56). Biomarkers of intestinal and systemic inflammation and mucosal integrity were highly abnormal in most children at baseline and abnormal values persisted in all three arms. The enteropathy in complicated SAM did not respond to either standard feeds or alternative therapeutic feeds administered for up to 14 days. A better understanding of the pathogenesis of the gut pathology in complicated SAM is an urgent priority to inform the development of improved therapeutic interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".