MétaCan
Menu
Back to cohort
Record W2750707875 · doi:10.3389/fendo.2017.00218

Food Addiction in Patients with Newly Diagnosed Type 2 Diabetes in Northeast China

2017· article· en· W2750707875 on OpenAlexaff
Fan Yang, Aihua Liu, Yongze Li, Yaxin Lai, Guixia Wang, Chenglin Sun, Guang Sun, Zhongyan Shan, Weiping Teng

Bibliographic record

VenueFrontiers in Endocrinology · 2017
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsMemorial University of Newfoundland
FundersNational Natural Science Foundation of China
KeywordsMedicineType 2 Diabetes MellitusBody mass indexInternal medicineLogistic regressionFood addictionDiabetes mellitusObesityEndocrinology

Abstract

fetched live from OpenAlex

Aim: The aim of the study was to explore the prevalence of food addiction (FA) in individuals with newly diagnosed type 2 diabetes mellitus (T2DM) in China and to analyze risk factors of FA. Methods: A total of 624 subjects [312 individuals with newly diagnosed T2DM, 312 age-matched and body mass index (BMI)-matched healthy participants] were recruited. All participants were asked to complete the Yale Food Addiction Scale (YFAS) and received physical and lab examinations. The T2DM group was further divided into a FA group and a non-FA group. Results: Of the patients with newly diagnosed T2DM, 8.6% (27/312) met the FA diagnostic criteria proposed by the YFAS (7.6% in men and 10.1% in women, P=0.43), while 1.3% (4/312) met the criteria in the control group. Logistic regression analysis showed that FA in the T2DM group was positively related to BMI and negatively related to age. T2DM with FA had a significantly higher Uric Acid (UA) and a trend towards a lower Thyroid Stimulating Hormone(TSH) level. Conclusion: Both men and women with newly diagnosed T2DM, especially in northeast China, were more likely to suffer from FA. T2DM patients with FA were younger and had higher UA.

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 score0.017
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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

Citations35
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in EndocrinologySame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207