Are innovative ready to use therapeutic foods more effective, accessible and cost-efficient than conventional formulations? A review
Bibliographic record
Abstract
Ready to Use Therapeutic Foods (RUTFs) are used in international food assistance strategies as a safe and effective way of treating children suffering from severe acute malnutrition (SAM). Though the peanut-based formulation has a proven track record in terms of efficacy in treating SAM around the world, the conventional formulation is not without challenges. Concerns regarding cost, the availability of local ingredients, the presence of aflatoxin, shifting global supply patterns, and dietary appropriateness of the peanut-based RUTF have encouraged researchers to experiment with other lipid sources in formulations. This shift requires not only changes to RUTF formulations, but also changes to supply chain activities. The goal of this review is to first, provide an update on the efficacy of recently trialed non-peanut RUTF formulations in treating SAM in infants and children and second, to review recent UN agency led interventions into local/regional RUTF supply chains and programmatic capacity. Based on published documents (2017–2019), this review flags three significant issues requiring further attention from the international food assistance community: the need for follow-up studies of children treated for SAM with RUTFs in programmatic countries, a regional customization of Community-Based Management of Acute Malnutrition (CMAM) protocols to maximize cost effectiveness and programmatic coverage, and an increase in the number of studies focusing on the acceptability of non-peanut RUTF formulations by the infants and children in low and medium income countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".