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Record W4411923760 · doi:10.1016/j.ajcnut.2025.06.030

Neglected micronutrients—considering a broader set of vitamins and minerals in public health nutrition programs worldwide: a narrative review

2025· review· en· W4411923760 on OpenAlexaff
Kenneth H. Brown, Sonja Y. Hess, Sophie E. Moore, Gerald F. Combs, Kevin D. Cashman, Helene McNulty, Lindsay H. Allen, Nancy F. Krebs, Christine M Pfeiffer, Michael E. Rybak, Saskia Osendarp

Bibliographic record

VenueAmerican Journal of Clinical Nutrition · 2025
Typereview
Languageen
FieldNursing
TopicVitamin K Research Studies
Canadian institutionsNutrition International
FundersBill and Melinda Gates Foundation
KeywordsMicronutrientNarrativeSet (abstract data type)Narrative reviewPublic healthEnvironmental healthPsychologyMedicineComputer scienceIntensive care medicineArtPathologyLiterature

Abstract

fetched live from OpenAlex

Several essential vitamins and minerals whose deficiencies are associated with metabolic and functional disorders, including increased morbidity and mortality from both communicable and noncommunicable diseases, are not being adequately addressed by large-scale, public health nutrition programs worldwide. These neglected micronutrients include thiamine, riboflavin, niacin, pyridoxine, vitamin B-12, vitamin D, vitamin K, calcium, selenium, and possibly others. In this narrative review, our objectives are to describe briefly the health implications of each of these deficiencies, summarize the limited available information on their epidemiology, and suggest possible approaches to address them. We conclude that more information, based on dietary assessments, nutritional biomarker surveys, and systematic surveillance of associated health conditions, is needed. Appropriate intervention programs, including changes in food systems to provide wider access to nutrient-rich foods, food fortification and targeted supplementation, should be implemented in settings where these deficiencies are confirmed.

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)0.003
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.880
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.153
GPT teacher head0.491
Teacher spread0.338 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations10
Published2025
Admission routes1
Has abstractyes

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