Impact of Dietary Diversification on the Prognostic Inflammatory and Nutritional Index in School-Age Children in the Nawa Region (Côte d’Ivoire)
Bibliographic record
Abstract
The objective of this work was to study the impact of food diversification based on sweet potato, soybean, and cowpea on the prognostic inflammatory and nutritional index (PINI) in school-aged children in the Nawa region. This study took place from October 2017 to May 2018 among 240 pupils aged 6 to 12, divided into four groups of 60. Four types of meals were proposed: rice with tomato soup and fish (group 1), sweet potato porridge enriched with green soybeans (group 2), sweet potato porridge enriched with white cowpea (group 3), or sweet potato porridge accompanied by white cowpea with green soybeans (group 4). There were three blood samples: before eating meals (phase 0), the end of the first trimester (phase 1), and the end of the second trimester (phase 2). Blood assay for C-reactive protein (CRP), orosomucoid, albumin, and prealbumin was performed using COBAS c311 analyzer. PINI was calculated. Groups 3 and 4 showed a slight increase in albumin values (42.24 ± 0.95 g/L and 41.51 ± 1.71 g/L, respectively) compared to group 1. CRP decreased from phase 1 for group 1 (2.06 ± 0.26 mg/L) and group 4 (2.38 ± 0.36 mg/L). Orosomucoid increased insignificantly (p > 0.05) in group 3 (0.74 ± 0.04 g/L) and group 4 (0.71 ± 0.04 g/L). PINI was reduced by 0.37 (group 1), 0.36 (group 2), 0.46 (group 3) and 0.44 (group 4). Food diversification based on sweet potato and white cowpea has a positive impact on PINI in more than 80% of pupils.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".