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Record W1998314095 · doi:10.1017/s0007114512003200

A symposium and workshop report from the Global Nutrition and Epidemiologic Transition Initiative: nutrition transition and the global burden of type 2 diabetes

2012· article· en· W1998314095 on OpenAlexfundno aff
Josiemer Mattei, Vasanti Malik, Nicole M. Wedick, Hannia Campos, Donna Spiegelman, Walter C. Willett, Frank B. Hu

Bibliographic record

VenueBritish Journal Of Nutrition · 2012
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
FundersNational Institute of Dental and Craniofacial ResearchJohns Hopkins Bloomberg School of Public HealthUniversity of TorontoJohns Hopkins UniversityBristol-Myers SquibbJoslin Diabetes CenterEmory UniversityBrigham and Women's Hospital
KeywordsNutrition transitionPsychological interventionEpidemiological transitionType 2 diabetesMedicineGerontologyEnvironmental healthGlobal healthDiabetes mellitusPublic healthObesityNursingOverweightEndocrinology

Abstract

fetched live from OpenAlex

The present report summarises the symposium 'Nutrition Transition and the Global Burden of Type 2 Diabetes' and a workshop on strategies for dietary interventions to prevent type 2 diabetes held by the Global Nutrition and Epidemiologic Transition Initiative, Boston, MA, USA in November 2011. The objectives of this event were to bring attention to the global epidemic of type 2 diabetes in light of the ongoing nutrition transition worldwide, especially in low- and middle-income countries, and to highlight the present evidence on key dietary risk factors contributing to the global diabetes burden. The meeting put forward ideas for further research on this topic and discussed practical recommendations to design and implement culturally appropriate dietary interventions with a focus on improving carbohydrate quality to help alleviate this growing health problem.

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)0.014
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0050.003
Open science0.0020.005
Research integrity0.0100.013
Insufficient payload (model declined to judge)0.0090.004

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 head0.025
GPT teacher head0.286
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations35
Published2012
Admission routes1
Has abstractyes

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