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Record W4417248561 · doi:10.1080/09581596.2025.2598713

Consumption of dewaxed brown rice for a six-month period improves cognitive function in older adults: an open-label trial

2025· article· en· W4417248561 on OpenAlexaff
Michio Takahashi, Yuji Takano, Keisuke Kokubun, Naoki Nishiyama, Keiji Saika, Yasuyuki Taki

Bibliographic record

VenueCritical Public Health · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsBrown riceCognitionWhite riceCognitive declineCognitive testConsumption (sociology)Effects of sleep deprivation on cognitive performance

Abstract

fetched live from OpenAlex

Brown rice has received attention for its potential to improve cognitive function, but evidence from human subjects is scarce. Therefore, we conducted an open-label trial to examine the effectiveness of consuming low doses of dewaxed brown rice for six months in improving or maintaining cognitive function in older adults without cognitive impairment. Fifty-six cognitively healthy older people (≥60 years old) were recruited for this study; participants were divided into groups (consuming dewaxed brown rice or white rice) and asked to consume each type of rice four times per week for six months. We evaluated cognitive function before and after the intervention period to examine the effects of consuming dewaxed brown rice on cognitive function. Our analyses showed that the total score on the Frontal Assessment Battery was improved in the brown-rice group but not in the white-rice group. Post-hoc analysis demonstrated that the total score on the Frontal Assessment Battery improved in the brown rice group but not the white rice group after intervention. In contrast, the total score on the Mini-Mental State Examination was not changed in either group. These findings suggest that consuming low doses of dewaxed brown rice improved executive function in older people.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.119
GPT teacher head0.406
Teacher spread0.287 · 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 designOther design
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

Citations0
Published2025
Admission routes1
Has abstractyes

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