Hydrothermally-treated soybean-fortified maize-based nsima (stiff porridge) could contribute towards alleviating seasonal body weight loss in farming communities in sub-Saharan Africa
Bibliographic record
Abstract
Objective: This study explored the use of hydrothermally-treated soybean-fortified maize-based stiff porridge (nsima) in managing body weight losses among the farming family community in Malawi during the labour-intensive cropping (growing) season. We hypothesized that soybean-fortified maize-based nsima could prevent seasonal body weight losses in farming communities during labour-intensive seasons better than conventional 100% maize nsima. Research methods & procedures: A single-blind parallel dietary intervention 90-day study. During energy stress months, 42 farming households in Malawi were supplied with 15 kg of blind formulation of soybean-fortified maize flour (soybean: maize, 1:4, wt/wt) per person per month except for under-fives who were allotted half the quantity. Forty households were provided with equivalent quantities of 100% maize flour and served as control. Body weights of participants were taken at baseline and endpoint. Results: After 3 months, the experimental group registered 3.7, 4.2, 2.9, and 5.2% statistically higher body weight compared to the controls for the under-five, the 5-9-year-olds, the 10-19-year-olds, and the >20-year-olds, respectively. Conclusion: Soybean-fortified stiff porridge could feasibly be used to alleviate wasting among the resource-constraint populace in Malawi and many other parts of sub-Saharan Africa that rely on maize as a major staple.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".