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Record W4387566800 · doi:10.58931/cdet.2023.1317

The Role of Plant-Based Diets in the Management of Type 2 Diabetes

2023· article· en· W4387566800 on OpenAlexaff
Heidi Dutton

Bibliographic record

VenueCanadian Diabetes & Endocrinology Today · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsCanadian Fitness and Lifestyle Research InstituteUniversity of Ottawa
Fundersnot available
KeywordsVegan DietFood scienceMealObesityBiologyWhole foodFish <Actinopterygii>Food productsBiotechnologyMedicine

Abstract

fetched live from OpenAlex

A whole food plant-based (WFPB) diet is generally defined as a diet rich in fruits, vegetables, whole grains, legumes, nuts and seeds, and herbs and spices. Many define a WFPB diet as being exclusively plant-based with no animal products, excluding all red meat, poultry, fish, eggs, and dairy products. Other sources define it as a plant-forward dietary pattern that may still include small amounts of meat, eggs or dairy. A WFPB dietary pattern focuses on unprocessed plant foods, while avoiding processed foods containing refined grains, refined oils and added sugars. Figure 1 depicts an example of a balanced WFPB meal. On a practical level, it is important to distinguish a WFPB diet from a vegan diet, which eliminates all animal products but may include processed vegan foods (e.g., plant-based meats, pastries and fried foods). However, in the scientific literature, the term “vegan” is often used, and at times it is difficult to assess the amount of processed food included in diets of vegans included in observational studies. This paper will focus primarily on the evidence for an exclusively WFPB dietary pattern in the prevention and management of Type 2 diabetes mellitus (T2DM) and obesity. However, given certain limitations in the literature, some data on vegan diets and plant-rich but not exclusively plant-based diets will also be included.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.050
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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