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Record W2751144895 · doi:10.2147/cia.s145247

Cognitive and behavioral evaluation of nutritional interventions in rodent models of brain aging and dementia

2017· review· en· W2751144895 on OpenAlexaff
Devin Wahl, Sean C. P. Coogan, Samantha M. Solon‐Biet, Rafael de Cabo, James Haran, David Raubenheimer, Victoria C. Cogger, Mark P. Mattson, Stephen J. Simpson, David G. Le Couteur

Bibliographic record

VenueClinical Interventions in Aging · 2017
Typereview
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsUniversity of Alberta
FundersNational Health and Medical Research CouncilNational Institute on AgingNational Institutes of HealthAmerican Australian Association
KeywordsDementiaMorris water navigation taskMedicineBarnes mazeCognitionNeuroscienceEpisodic memorySemantic memoryPsychological interventionBrain agingMemory impairmentPsychologyPsychiatryDiseasePathologySpatial learning

Abstract

fetched live from OpenAlex

Evaluation of behavior and cognition in rodent models underpins mechanistic and interventional studies of brain aging and neurodegenerative diseases, especially dementia. Commonly used tests include Morris water maze, Barnes maze, object recognition, fear conditioning, radial arm water maze, and Y maze. Each of these tests reflects some aspects of human memory including episodic memory, recognition memory, semantic memory, spatial memory, and emotional memory. Although most interventional studies in rodent models of dementia have focused on pharmacological agents, there are an increasing number of studies that have evaluated nutritional interventions including caloric restriction, intermittent fasting, and manipulation of macronutrients. Dietary interventions have been shown to influence various cognitive and behavioral tests in rodents indicating that nutrition can influence brain aging and possibly neurodegeneration.

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)0.002
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.688
GPT teacher head0.646
Teacher spread0.041 · 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

Citations108
Published2017
Admission routes1
Has abstractyes

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