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Evaluation of Abdominal Obesity by a Portable Stereo Vision Body Imaging System

2010· article· en· W2292578976 on OpenAlexaboutno aff
Jeanne Freeland‐Graves, M. Reese Pepper, Jane Lee, Yeyi Zhu, Drew Bean, Ming Yao, Bugao Xu

Bibliographic record

VenueThe FASEB Journal · 2010
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsMedicineIntraclass correlationUmbilicus (mollusc)Magnetic resonance imagingWaistAbdomenNuclear medicineCalipersCircumferenceObesityRadiologyInternal medicineSurgeryMathematics

Abstract

fetched live from OpenAlex

Visceral fat stores in the abdomen are related to increased risk for insulin resistance and cardiovascular disease. This metabolically active fat depot is commonly measured via Magnetic Resonance Imaging (MRI), although this method is costly and burdensome for subjects. The focus of this study was the evaluation of a portable body imaging system for analysis of abdominal obesity. Caucasian and Hispanic women fasted for 4 hours before measurement of anthropometrics, 8‐camera stereo vision body imaging, and MRI. Intraclass correlation coefficient of MRI for a subset of subjects showed strong agreement between two observers for labeling of visceral and subcutaneous fat with SliceOMatic version 4.3 (TomoVision, Magog, Quebec) (Intraclass Correlation Coefficient 1.000 and 0.999 for subcutaneous and visceral fat, respectively, p<0.01). Stepwise linear regression analysis showed that girths at the thigh and umbilicus as measured by the portable body imaging system were strong predictors of visceral fat volume (Adjusted r 2 =0.709, p<0.01). This represented marked improvement over traditional manual prediction methods sagittal diameter and waist circumference by caliper and tape (Adjusted r 2 =0.651 and 0.531, respectively, p<0.01). These results indicate that the portable body imaging system is an effective means to evaluate central adiposity. Supported by NIH 1 R21 DK081206‐02.

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)0.007
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
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 integrity0.0000.001
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 head0.019
GPT teacher head0.305
Teacher spread0.286 · 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 designBench or experimental
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

Citations0
Published2010
Admission routes1
Has abstractyes

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