Application and significance of fortification in prevention of micronutrient deficiency-induced diseases
Bibliographic record
Abstract
Fortification is defined as adding of one or more essential elements to food article, regardless of whether it has been already added to food or not, in order to prevent or correct deficiency of one or more nutrients in the general population or specific population group. Food fortification with minerals and vitamins helps eliminate diseases such as goiter, rickets, beriberi, and pellagra. Significant results have been also achieved in prevention of anemia and vitamin A deficiency. The aforementioned deficiencies can be prevented and eliminated by means of appropriate and diverse nutrition and supplementation of deficient micronutrients, but on the national level, food fortification is the best solution. Two basic conditions for the application of fortification are the following: that the food article is in wide use and that it is cheap (available). The purpose of our paper was to show the results achieved by means of fortification in various countries in order to build up the basis for similar propositions in our country (Serbia and Montenegro). Owing to fortification in Asia, the number of cretinism cases has been reduced by half while sugar fortification significantly reduced the number of children with vitamin A deficiency. For more than 50 years, flour fortification with iron in order to prevent its deficiency and anemia, has been successfully applied in the United States and Canada, and as of recently in some countries of Africa and South America. The analysis of the results leads to the conclusion that food fortification has had beneficial health effects in the communities where it has been applied.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".