Nutrient and Anti-nutrient Profile of a Local Formula from Sorghum, Peanut, Honey and Ghee (Metu2) for Treatment of Severe Acute Malnutrition
Bibliographic record
Abstract
World over, we are still struggling with persistent acute malnutrition levels; an estimated 17 million preschool children suffer from SAM, roughly the same figures as reported in 2013, a trend depicting insufficient progress towards the 2025 World Health Assembly. One such affected area is Karamoja Region in North Eastern Uganda. Partly, the trend could be attributed to unsustainable interventions like RUTF. Formulas from locally available foods could provide not only an affordable but also a culturally acceptable and effective home based solution. Locally available sorghum, peanut, honey and ghee in North Eastern Uganda, is such a potential local formula. The nutritional and anti-nutritional profile of this local formula(metu2) was compared to plumpy-nut. Standard official analytical methods were used. Proximate composition was comparable and within the WHO recommendations for therapeutic formulas. Local formula(metu2) had a comparatively high energy content, 528kcal/100g to 509kcal in plumpynut. Vitamin A and K contents were below the WHO recommendations in local formula while Na, Mg and essential fatty acids were comparable and within the contents needed for SAM recovery. Zn was comparatively higher in plumpy-nut but levels in both formulas were below the recommendations. Trypsin inhibitors, phytates and condensed tannins were higher in local formula while aflatoxins were within the limits but not for plumpynut. Though lacking in critical K, Zn and Vitamin A, local formula(metu2) was comparable to plumpy-nut and its efficacy to sustain recovery from SAM needs to be studied.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".