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Record W2973945800 · doi:10.1503/cmaj.190124

Association of predicted lean body mass and fat mass with cardiovascular events in patients with type 2 diabetes mellitus

2019· article· en· W2973945800 on OpenAlexvenueno aff
Zhenhua Xing, Liang Tang, Jian Chen, Junyu Pei, Pengfei Chen, Zhenfei Fang, Shenghua Zhou, Xinqun Hu

Bibliographic record

VenueCanadian Medical Association Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsnot available
Fundersnot available
KeywordsType 2 Diabetes MellitusLean body massMedicineDiabetes mellitusBody mass indexInternal medicineObesityAssociation (psychology)Type 2 diabetesEndocrinologyBody weightPsychology

Abstract

fetched live from OpenAlex

ardiovascular disease (CVD) is the leading cause of mortality in the United States and worldwide. 1,2 Among several modifiable risk factors for CVD, obesity is recognized as a major risk factor. Body mass index (BMI) is a good measure of obesity; many epidemiologic studies have found that obesity, evaluated by BMI, is associated with increased risk of CVD. owever, recent studies have found that BMI is an imperfect measure of obesity and it does not discriminate between lean body mass and fat mass. Lean body mass (mainly skeletal muscles) has a protective role, whereas fat mass is detrimental. eople with the same BMI may have different body compositions. Furthermore, patients with type 2 diabetes mellitus (T2DM) tend to be overweight and obese compared with those who do not have the disease. 9 Type 2 diabetes mellitus has been shown to be associated with lower lean body mass. The factors (e.g., dietary habit, exercise and age) that may affect lean body mass and fat mass are completely different in these 2 populations. Therefore, lean body mass and fat mass may play different roles in these groups. 7]

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)0.001
metaresearch head score (Gemma)0.001
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 score0.018
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.002
GPT teacher head0.171
Teacher spread0.169 · 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

Citations45
Published2019
Admission routes1
Has abstractyes

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