MétaCan
Menu
Back to cohort
Record W2932346162 · doi:10.22230/ijdrp.2019v1n1a11

The Use of Plant-Based Diets for Obesity Treatment

2019· article· en· W2932346162 on OpenAlexaff
Neal D. Barnard, Hana Kahleová, Susan Levin

Bibliographic record

VenueInternational Journal of Disease Reversal and Prevention · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsPacific Centre for Reproductive Medicine
Fundersnot available
KeywordsVegan DietPostprandialWeight lossObesityOmnivoreObservational studyEnergy densityMedicineEnergy expenditureBody weightFood scienceClinical trialBiologyEndocrinologyInternal medicineDiabetes mellitus

Abstract

fetched live from OpenAlex

In observational studies, individuals following vegetarian, particularly vegan, diets have healthier body weights, on average, compared with those following omnivorous diets. In clinical trials, vegetarian and vegan diets lead to significant weight loss, even in the absence of physical exercise or limits on energy intake. The mechanisms by which plant-based diets cause weight loss appear to be (1) reduced dietary energy density, as a result of their high fiber and low fat content, and (2) increased postprandial energy expenditure. The degree of weight loss associated with plant-based diets in clinical trials is as great as that with other popular diet patterns, and favorable changes in overall nutrition, plasma lipid concentrations, and blood pressure are also observed. Acceptability with, and adherence to vegan diets has been studied in varied populations in clinical trials and is similar to that of other therapeutic diets.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.250
Teacher spread0.233 · 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 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

Citations15
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Disease Reversal and PreventionSame topicAgriculture Sustainability and Environmental ImpactFrench-language works237,207