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Record W3165456351 · doi:10.3390/nu13061844

Beyond Nutrient Deficiency—Opportunities to Improve Nutritional Status and Promote Health Modernizing DRIs and Supplementation Recommendations

2021· review· en· W3165456351 on OpenAlexaff
Michael I. McBurney, Jeffrey B. Blumberg, Rebecca B. Costello, Manfred Eggersdorfer, John W. Erdman, William S. Harris, Elizabeth J. Johnson, Susan Hazels Mitmesser, Robert C. Post, Deshanie Rai, Leon J. Schurgers

Bibliographic record

VenueNutrients · 2021
Typereview
Languageen
FieldNursing
TopicVitamin K Research Studies
Canadian institutionsUniversity of Guelph
FundersCystinosis Research Network
KeywordsMedicineDietary Reference IntakeBiomarkerNutrientVitamin D and neurologyEnvironmental healthDiseaseEicosapentaenoic acidBiotechnologyBiologyInternal medicinePolyunsaturated fatty acidFatty acid

Abstract

fetched live from OpenAlex

The US Dietary Guidelines for Americans (DGA) provide dietary recommendations to meet nutrient needs, promote health, and prevent disease. Despite 40 years of DGA, the prevalence of under-consumed nutrients continues in the US and globally, although dietary supplement use can help to fill shortfalls. Nutrient recommendations are based on Dietary Reference Intakes (DRIs) to meet the nutrient requirements for nearly all (97 to 98 percent) healthy individuals in a particular life stage and gender group and many need to be updated using current evidence. There is an opportunity to modernize vitamin and mineral intake recommendations based on biomarker or surrogate endpoint levels needed to 'prevent deficiency' with DRIs based on ranges of biomarker or surrogate endpoints levels that support normal cell/organ/tissue function in healthy individuals, and to establish DRIs for bioactive compounds. We recommend vitamin K and Mg DRIs be updated and DRIs be established for lutein and eicosapentaenoic and docosahexaenoic acid (EPA + DHA). With increasing interest in personalized (or precision) nutrition, we propose greater research investment in validating biomarkers and metabolic health measures and the development and use of inexpensive diagnostic devices. Data generated from such approaches will help elucidate optimal nutrient status, provide objective evaluations of an individual's nutritional status, and serve to provide personalized nutrition guidance.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
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 head0.124
GPT teacher head0.417
Teacher spread0.293 · 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 designNot applicable
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

Citations15
Published2021
Admission routes1
Has abstractyes

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