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Double Fortified Salt in India: Coverage, Efficacy and Way Forward

2018· article· en· W4300546346 on OpenAlexaff
M.G. Venkatesh Mannar, J K Raman

Bibliographic record

VenueIndian Journal of Community Health · 2018
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineEnvironmental healthIodised saltAnemiaFortificationIodine deficiencyDistribution (mathematics)FerrousFood fortificationIron deficiencyPopulationInternal medicineFood science

Abstract

fetched live from OpenAlex

Iron deficiency remains the world’s most widespread nutritional disorder and India is one of the countries very worst afflicted. India has successfully reduced the burden of iodine-deficiency disorders through mandatory iodization of salt for more than 20 years. This has resulted in a significant decrease in the prevalence of iodine deficiency diseases. Building on the success with iodization, double fortification of salt with iodine and iron is gaining ground and can be integrated with established iodization processes. DFS contemplates the creation and distribution of a powerful innovative product with demonstrated health effects, building on existing distribution platforms for salt through public distribution channels targeted to some of the most impoverished populations in the country at minimal expense and without requiring changes in cultural habits and compliance. Two formulations have been approved by Food Safety and Standard Authority of India (FSSAI) with iron either in the form of encapsulated ferrous fumarate or ferrous sulphate. A meta-analysis showed that DFS increased hemoglobin concentrations significantly. This intervention as part of a broader anemia strategy has the potential to effect large-scale anemia reduction across populations in India on a permanent and economically self-sustaining basis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.343
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

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