A protocol for a proof-of-concept randomized control trial testing increased protein quantity and quality in ready-to-use therapeutic food in improving linear growth among 6-23-month-old children with severe wasting in Malawi
Bibliographic record
Abstract
INTRODUCTION: Ready-to-use therapeutic foods (RUTFs) have successfully promoted recovery from severe wasting and increased treatment coverage. However, RUTFs do not sufficiently improve linear growth, leaving many survivors of severe wasting at risk of persistent stunting, which is associated with high mortality risk, poor child development and non-communicable diseases in adulthood. High protein quantity and quality can stimulate linear growth. AIM: The trial aims to assess whether higher-protein-RUTF leads to higher concentrations of markers of linear growth compared to standard RUTF among 6-23 months old children with severe wasting. METHODS: We designed a higher protein quantity and quality RUTF for a proof-of-concept (PoC) double-blind randomized controlled trial. OUTCOMES: The primary outcome is a change in insulin-like growth factor-1 (IGF-1), a hormone positively associated with linear growth after four weeks of treatment. Secondary outcomes include changes in ponderal and linear growth and in body composition from baseline to eight weeks later; plasma amino acid profile at four weeks; acceptability and safety. IMPLICATIONS: These findings will help in informing the potential impact of increased protein in RUTF on linear growth when treating severe wasting towards conducting a larger clinical trial. TRIAL REGISTRATION: The trial has been registered on clinicaltrial.gov (NCT05737472).
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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 | 0.051 | 0.046 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.137 | 0.024 |
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".