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Record W2765177862 · doi:10.3390/antiox6040081

The Role of Food Antioxidants, Benefits of Functional Foods, and Influence of Feeding Habits on the Health of the Older Person: An Overview

2017· review· en· W2765177862 on OpenAlexaff
Douglas W. Wilson, Paul Nash, Harpal S. Buttar, K. Griffiths, Ram B. Singh, Fabien De Meester, Rie Horiuchi, Toru Takahashi

Bibliographic record

VenueAntioxidants · 2017
Typereview
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFunctional foodHealth foodFood scienceHealth benefitsEnvironmental healthPsychologyMedicineChemistryTraditional medicine

Abstract

fetched live from OpenAlex

This overview was directed towards understanding the relationship of brain functions with dietary choices mainly by older humans. This included food color, flavor, and aroma, as they relate to dietary sufficiency or the association of antioxidants with neurodegenerative diseases such as dementia and Alzheimer's disease. Impairment of olfactory and gustatory function in relation to these diseases was also explored. The role of functional foods was considered as a potential treatment of dementia and Alzheimer's disease through inhibition of acetylcholinesterase as well as similar treatments based on herbs, spices and antioxidants therein. The importance of antioxidants for maintaining the physiological functions of liver, kidney, digestive system, and prevention of cardiovascular diseases and cancer has also been highlighted. Detailed discussion was focused on health promotion of the older person through the frequency and patterns of dietary intake, and a human ecology framework to estimate adverse risk factors for health. Finally, the role of the food industry, mass media, and apps were explored for today's new older person generation.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.142
GPT teacher head0.356
Teacher spread0.213 · 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 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

Citations230
Published2017
Admission routes1
Has abstractyes

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