The impact of Quality Protein Maize (QPM) on school children's weight and height: Results from an effectiveness trial in Colombia
Bibliographic record
Abstract
QPM has a greater concentration of lysine and tryptophan and has been proven more efficacious than common maize (CM) in improving the weight and height of preschool children. The objective was to evaluate the impact on school children's weight and height of consuming QPM through a government‐supported school feeding program in rural Colombia. In a quasi‐experimental design, schools were assigned to receive QPM seed for planting (n=4), QPM for consuming (n=3), or CM for consuming (n=5). At baseline and 12 mo later at endline, children's (n=274) weight and height were measured. During the 2‐mo holiday period, children in the consumption groups were provided sufficient maize for the whole family. At baseline, children were 88 ± 16 (average ± SD) mo, 114.2 ± 8.0 cm tall, and weighed 21.6 ± 4.0 kg; 33.7% were stunted (HAZ<−2). Children in the seed group were 3 cm shorter than children in the consumption groups. By endline, children gained an average of 3 kg and 6 cm, independent of intervention group. These results did not change after adjusting for the cluster design and covariates. Tryptophan levels were 28% higher in QPM than CM, schools provided maize 38–166 school days during the academic year, and maize consumed by children ranged from 16 to 110 g/day. In conclusion, in the context of a governmental school feeding program, QPM did not improve school children's weight and height compared with CM. Funding: AgroSalud (CIDA 7034161).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".