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Record W4390599693 · doi:10.1016/j.ajcnut.2023.10.030

The effects of inulin-type fructans on cardiovascular disease risk factors: systematic review and meta-analysis of randomized controlled trials

2024· review· en· W4390599693 on OpenAlexafffund
Jhalok Ronjan Talukdar, Matthew Cooper, Lyuba Lyutvyn, Dena Zeraatkar, Rahim Ali, Rachel Berbrier, Sabrina Janes, Vanessa Ha, Pauline Darling, Mike Xue, A. Chu, Fariha Chowdhury, Hope E. Harnack, Louise Huang, Mikail Malik, Jacqui Powless, Florence Lavergne, Xuehong Zhang, Shelley Ehrlich, David J.A. Jenkins, John L. Sievenpiper, Laura Banfield, Lawrence Mbuagbaw, Russell J. de Souza

Bibliographic record

VenueAmerican Journal of Clinical Nutrition · 2024
Typereview
Languageen
FieldNursing
TopicMicrobial Metabolites in Food Biotechnology
Canadian institutionsSt. Joseph’s Healthcare HamiltonHamilton Health SciencesUniversity of TorontoSt. Michael's HospitalMcGill University Health CentreUniversity of OttawaUniversity of AlbertaPopulation Health Research InstituteQueen's UniversityMcMaster UniversityImpact
FundersInternational Nut and Dried Fruit CouncilOntario Ministry of Research, Innovation and ScienceInstitute for the Advancement of Food and Nutrition SciencesSoy Nutrition InstituteCanadian Institutes of Health ResearchNational Honey BoardPeanut InstituteCanada Foundation for InnovationOntario Research FoundationUnited Soybean BoardFlax Council of CanadaCanadian Society of Endocrinology and MetabolismIan's Friends FoundationCanadian Diabetes AssociationU.S. Department of Agriculture
KeywordsMedicineInternal medicineMeta-analysisRandomized controlled trialCochrane LibraryPlaceboBody mass indexWaistBlood pressureLipid profileCholesterol

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.009
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.019
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0220.026
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.083
GPT teacher head0.427
Teacher spread0.344 · 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 designMeta-analysis
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

Citations19
Published2024
Admission routes2
Has abstractno

Explore more

Same venueAmerican Journal of Clinical NutritionSame topicMicrobial Metabolites in Food BiotechnologyFrench-language works237,207