Should India Use Commercially Produced Ready to Use Therapeutic Foods (RUTF) for Severe Acute Malnutrition (SAM)
Bibliographic record
Abstract
Globally, nearly 20 million children under five suffer from Severe Acute Malnutrition (SAM), a condition which contributes to one million child deaths annually. In India 48% of children under five years of age are stunted and 43 percent are underweight; almost 8 million suffer from SAM. Malnutrition is not a new problem in India, nor is SAM. Several hospitals and non-government organizations are engaged in community-based management of malnutrition using locally produced/procured and locally processed foods along with intensive nutrition education. These programs enable parents to meet the nutritional requirements of their children with foods that are available at low cost. The Supreme Court of India has also directed the government to universalize the Integrated Child Development Scheme and provide one hot cooked meal to children under six years of age to supplement their nutrition.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".