Are innovative ready to use therapeutic foods more effective, accessible and cost-efficient than conventional formulations? A review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it