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The Role of Radiologic Methods in Assessing Body Composition and Related Metabolic Parameters

2009· review· en· W2026756890 on OpenAlexaff
Gilles Plourde

Bibliographic record

VenueNutrition Reviews · 2009
Typereview
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsMedicineComposition (language)Fat massMetabolic syndromeBody weightPathologyObesityInternal medicine

Abstract

fetched live from OpenAlex

The measurement of body composition and related metabolic parameters has become an important issue in clinical nutrition. Numerous techniques to assess visceral fat, which is strongly associated with metabolic disorders, have been developed. Other techniques focus mainly on the measurement of specific body components related to metabolic disturbances. This paper reviews methods that directly assess body composition and associated metabolic parameters. The principles of these methods and their accuracy, reproducibility and safety, as well as the clinical implications of their use, are discussed. Recent studies have documented the safety and efficacy of radiologic methods of assessing visceral fat, muscle mass, and morphology to obtain body composition data related to metabolic disturbances. Because these techniques have been documented to be safe and effective, clinicians should consider using them in the evaluation and follow-up of patients with various conditions.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
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 head0.098
GPT teacher head0.440
Teacher spread0.341 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations26
Published2009
Admission routes1
Has abstractyes

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