Ready-to-Use Therapeutic and Supplementary Foods in Ethiopia from 2006-2018: Scoping Review
Bibliographic record
Abstract
Background: Ready-to-Use Foods (RUFs) revolutionized treatment of acute malnutrition. Lipid-based Nutrient supplements (LNS) complement diets in the prevention of chronic undernutrition. We conducted a scoping review to understand Ethiopian uses of RUFs to inform future efforts by various parties engaged in formulation, production and delivery of RUFs. Methods: We searched PUBMED, MEDLINE and GOOGLE for published articles on Ready-to-Use Therapeutic Foods (RUTF), Ready-to-Use Supplementary Foods (RUSF) or LNS. We included all studies done in Ethiopia and published up to September 2018. Results: Of 23 studies in this review; 18 had quantitative and five had qualitative or mixed approaches; 9 studies dealt with RUTFs and 9 studies dealt with RUSFs in treatment of severe and moderate acute malnutrition or in supplementing existing diet to prevent chronic undernutrition. One study explored adherence to nutrition support programs of HIV-infected adults and another assessed supply-chain factors affecting availability of supplies. Three studies dealt with formulation and acceptability of novel RUTF or RUSF products from local ingredients. Fifteen of the 23 studies dealt with children with severe or moderate acute malnutrition whereas seven dealt with HIV-infected patients initiating antiretroviral therapy. Conclusions: RUFs are integral to management of acute malnutrition in Ethiopia, particularly among young children. A growing interest in the use of RUFs among HIV positive patients was noted. Challenges included food sharing, trading of RUFs as commodity, high cost of standard RUFs, stigma associated with RUF use, and disliking the taste of RUFs. These issues warrant the attention of nutrition support program providers and of industry in product development.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".