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Record W2111469856 · doi:10.1186/1475-2891-13-27

Fructose in obesity and cognitive decline: is it the fructose or the excess energy?

2014· article· en· W2111469856 on OpenAlexafffund
Laura Chiavaroli, Vanessa Ha, Russell J. de Souza, Cyril W.C. Kendall, John L. Sievenpiper

Bibliographic record

VenueNutrition Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsMcMaster UniversityUniversity of TorontoUniversity of SaskatchewanSt. Michael's Hospital
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesEuropean Association for the Study of DiabetesEuropean Foundation for the Study of DiabetesLoblaw Companies LimitedCanadian Nutrition SocietyNational Institutes of HealthSaskatchewan Pulse GrowersAgriculture and Agri-Food CanadaCanadian Institutes of Health ResearchAlmond Board of CaliforniaAmerican Heart AssociationDanoneCoca-Cola FoundationPepsiCoUniversity of South CarolinaCanadian Foundation for Dietetic ResearchAbbott LaboratoriesCalifornia Strawberry CommissionCanola Council of CanadaGeneral MillsWorld Health Organization
KeywordsFructoseMedicineConfoundingCognitionMetabolic syndromeObesityClinical nutritionDiabetes mellitusCognitive declineEndocrinologyInternal medicineFood sciencePsychiatryBiology

Abstract

fetched live from OpenAlex

We read with interest the review by Lakhan and Kirchgessner, proposing that high fructose intake promotes obesity, metabolic syndrome, diabetes, and cognitive decline. Their focus on the role of fructose seems premature due to confounding from energy and the heavy reliance on low quality evidence from animal models. There is a lack of high quality evidence directly assessing the role of fructose in cognitive decline. Although one cannot exclude the possibility of a link, it remains an unconfirmed hypothesis.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.022
GPT teacher head0.303
Teacher spread0.282 · 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 designObservational
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

Citations5
Published2014
Admission routes2
Has abstractyes

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