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Record W2051140637 · doi:10.1024/0300-9831.74.1.35

The Use of Iron-fortified Wheat Flour to Reduce Anemia Among the Estate Population in Sri Lanka

2004· article· en· W2051140637 on OpenAlexaff
Nestel, Nalubola, Sivakaneshan, Wickramasinghe, Atukorala, Wickramanayake

Bibliographic record

VenueInternational Journal for Vitamin and Nutrition Research · 2004
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsNutrition International
Fundersnot available
KeywordsFortificationAnemiaHemoglobinMedicineIron supplementationPopulationMicronutrientIron deficiencyEnvironmental healthFood fortificationBioavailabilityPediatricsFood scienceInternal medicineBiology

Abstract

fetched live from OpenAlex

The use of flour fortified with 66 mg/kg of electrolytic or reduced iron to reduce the prevalence of anemia was determined in a two-year, double-blind, controlled trial. The trial was conducted in Sri Lanka among preschoolers between 9 and 71 months old, primary schoolers 6 to 11 years old, and nonpregnant women. At baseline, 18.4% of the preschoolers had low hemoglobin (Hb) concentrations. Neither electrolytic nor reduced iron had an effect on Hb concentration among preschoolers. Only 7% of the primary schoolers were anemic at the start of the trial and, again, fortification had no effect on Hb concentration. Twenty-nine percent of women had a low Hb at outset and there was no evidence that fortification had an effect on Hb in this group. The findings from this study suggest that fortification of flour with electrolytic iron or reduced iron was not beneficial in reducing anemia in this population. This was probably due to the low prevalence of anemia and low bioavailability of the fortificant iron. Fortification with either iron fortificant was acceptable.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.697
Threshold uncertainty score0.172

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.082
GPT teacher head0.410
Teacher spread0.328 · 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

Citations53
Published2004
Admission routes1
Has abstractyes

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