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Record W4282843475 · doi:10.1093/cdn/nzac060.019

Nutritional Anemia Reductions in Women of Childbearing Age Due to Food Fortification

2022· article· en· W4282843475 on OpenAlexaffabout
Andrea D. Dorbu, Hannah Waddel, Manpreet Chadha, C. Christina Mehta, Mandana Arabi, Reneé H. Moore, Helena Pachón

Bibliographic record

VenueCurrent Developments in Nutrition · 2022
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsNutrition International
Fundersnot available
KeywordsFortificationAnemiaMicronutrientMedicineHemoglobinAnimal scienceNutrientConfidence intervalBiofortificationFood scienceInternal medicineChemistryBiology

Abstract

fetched live from OpenAlex

Review literature and conduct a meta-analysis to quantify changes in hemoglobin (Hb) and anemia prevalence among women of childbearing age after fortification of wheat flour, maize flour, rice and oil (singly or combined). Online databases were searched for English-language documents with no restrictions on location or publication date that included longitudinal, pre-post cross-sectional, efficacy and effectiveness studies. A Bayesian arm-based meta-analysis estimated mean change and probability of Hb and anemia improvement from 17 studies. Results were stratified by fortified food and nutrients added to food. There was a > 95% probability that fortified wheat flour improved Hb and reduced anemia; mean Hb increased by 3.39 g/L (95% Credible Interval (CI) –0.63, 7.17) and anemia decreased by 12.8 percentage points (pp) (95% CI –23, 0.9). Likewise, fortified rice had a > 65% probability of improving Hb and reducing anemia; mean Hb increased by 2.71 g/L (95% CI –4.88, 10.64) and anemia decreased by 16.9 pp (95% CI –81, 37.8). Conversely, fortified maize flour had < 45% probability of improving Hb and reducing anemia; mean Hb decreased by 2.88 g/L (95% CI−12.85, 7.24) and anemia increased by 13.5 pp (95% CI –133,164). There was a > 90% probability that fortifying maize flour, oil, rice, and/or wheat flour with iron, folic acid or multiple micronutrients (MM) improved Hb. Mean Hb increase was highest for iron fortification (3.93 g/L, 95% CI 0.50, 7.56), followed by folic-acid fortification (3.42 g/L, 95% CI –2.08, 9.56), and lowest for MM fortification (2.11 g/L, 95% CI 0.75, 3.68). There was a > 45% probability that fortifying with any nutrients reduced anemia. Mean anemia decrease was highest for iron at 17.3 pp (95% CI –78.2, 35), followed by folic acid at 7.2 pp (95% CI −32.5, 19.7); however, fortification with MM increased anemia by 1.2 pp (95% CI –9.8, 14.9). There was a high probability of fortification increasing hemoglobin concentration if wheat flour or rice are fortified independently, and if foods are fortified with iron alone, folic acid alone, or a combination of multiple micronutrients. Anemia reductions were greatest for fortified wheat flour and rice and for foods fortified with iron and folic acid. Global Affairs Canada.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.019
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.291
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2022
Admission routes2
Has abstractyes

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