Prospects of fortification of salt with iron and iodine
Bibliographic record
Abstract
Fortification of salt with iron has been developed by the National Institute of Nutrition (NIN) as a strategy for the control of iron deficiency anaemia (IDA) in India, similar to iodization of salt for control of iodine deficiency disorders (IDD). Stability of the iron fortified salt (IFS), its bioavailability and organoleptic evaluation of food items containing the IFS have been demonstrated. Acceptability and effectiveness of the IFS in school children and in multicentric community trials have been demonstrated. With the introduction of universal iodization of salt as a national policy in 1988, NIN has developed a formulation for double fortification (DFS) of salt with iodine and iron. The stability of the nutrients under laboratory conditions along with their bioavailability were found to be good but varying with the quality of salt used. The DFS has been evaluated in controlled trials in tribal communities and in residential school children. The findings of these studies are discussed. Overall, in these trials, DFS effectively controlled iodine deficiency but a clear impact on reducing anaemia was not demonstrated. In residential schoolchildren, increased urinary excretion of iodine as well as reduced anaemia were observed. The quality of salt has been found to be an important determinant of the stability of iodine in DFS. Further evaluation of this potentially important intervention is in progress.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".