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Record W3144517140 · doi:10.1080/10408398.2021.1895054

Neither soyfoods nor isoflavones warrant classification as endocrine disruptors: a technical review of the observational and clinical data

2021· review· en· W3144517140 on OpenAlexaff
Mark Messina, Sonia Blanco Mejía, Aedín Cassidy, Alison M. Duncan, Mindy S. Kurzer, Chisato Nagato, Martin J. J. Ronis, Ian Rowland, John L. Sievenpiper, Stephen Barnes

Bibliographic record

VenueCritical Reviews in Food Science and Nutrition · 2021
Typereview
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsUniversity of GuelphUniversity of Toronto
FundersSoy Nutrition Institute
KeywordsPhytoestrogensIsoflavonesMedicineObservational studyEndocrine systemAdverse effectPhysiologyEstrogenBioinformaticsInternal medicineBiologyHormone

Abstract

fetched live from OpenAlex

isoflavone exposure, but the existing data are reassuring. Adverse effects of isoflavone intake were not identified in children, but limited research has been conducted. After extensive review, the evidence does not support classifying isoflavones as endocrine disruptors.

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)0.005
metaresearch head score (Gemma)0.013
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 score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.453
GPT teacher head0.568
Teacher spread0.115 · 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

Citations69
Published2021
Admission routes1
Has abstractyes

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