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Record W3032979355 · doi:10.14740/jnr.v0i0.594

Neurocognitive Performance of African Americans and Hispanic Adults in Relation to Diet and Physical Activity: A Literature Review

2020· review· en· W3032979355 on OpenAlexvenueno aff
Valeriy Zvonarev, Sahil Mamtani, Sunita Yadav, Khadija Sharazi, Hussain Syed, Abhishek Giri, Aaron Rodas, Polina Tregubenko, Donald P. Kotler

Bibliographic record

VenueJournal of Neurology Research · 2020
Typereview
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsNeurocognitiveEthnic groupSocioeconomic statusMedicineAffect (linguistics)CognitionGerontologyPhysical activityEffects of sleep deprivation on cognitive performanceAfrican americanPopulationDemographyPsychologyEnvironmental healthPsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

As minorities, African Americans (AAs) and Hispanic adults face enormous discrepancies across many dimensions, including race/ethnicity, socioeconomic status, legal status, gender, insurance status, or severity of conditions. There is a lot of evidence in the literature about the relationship between lifestyle and cognitive functioning in older adults. However, due to the infancy of research in this area, the relationship between diet, physical activity and cognitive decline in ethnic minorities remains unclear. We discussed the neurocognitive changes associated with physical activity and/or dietary aspects in Hispanic and AA adults, and explored various factors that affect physical activity and dietary changes in this population. Our analysis confirmed the convincing link between certain dietary patterns, physical inactivity and poor cognitive performance in AA and Hispanic adults. This report allowed us to draw necessary conclusions regarding the structure of Jacobi Frailty Initiative in the Bronx, NY, USA. J Neurol Res. 2020;10(3):56-68 doi: https://doi.org/10.14740/jnr594

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.085
GPT teacher head0.423
Teacher spread0.339 · 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 designSystematic review
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

Citations1
Published2020
Admission routes1
Has abstractyes

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