Efficacy of Zinc Fortified and Fermented Wheat Flour (EZAFFAW): A randomized controlled trial protocol
Bibliographic record
Abstract
BACKGROUND: Zinc deficiency poses significant health risks, particularly in low-income settings. This study aims to evaluate the impact of agronomically zinc biofortified (fermented and non-fermented) and post-harvest wheat flour flatbread on zinc status and metabolic health in adolescents and adult women in rural Pakistan. METHODS: A four-arm triple-blind randomized controlled trial will be conducted in a rural district of Pakistan. Participants (adolescents aged 10-19 and adult women aged 20-40) will be assigned to receive fermented or unfermented high zinc agronomically biofortified wheat flour flatbread, post-harvest zinc-fortified wheat flour flatbread, or low zinc conventional whole wheat flour flatbread. The meal would be served once a day, six days a week for six months. The study aims to enroll 1000 participants and will be analyzed based on the intention-to-treat principle. The trial is registered with number NCT06092515. OUTCOMES: Primary outcomes will include serum zinc concentration and metabolic markers, while secondary outcomes include anthropometric measurements, blood pressure, and dietary intake. CONCLUSION: This trial will provide valuable insights into the efficacy of agronomically zinc biofortified wheat flour in improving zinc status and metabolic health. Findings may inform public health strategies to combat zinc deficiency in resource-limited settings.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.007 |
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".