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Record W2480096044 · doi:10.1139/apnm-2016-0238

The influence of subcutaneous fat on the relationship between body composition and ultrasound-derived muscle quality

2016· article· en· W2480096044 on OpenAlexvenueno aff
Eric D. Ryan, Nicholas W. Shea, Gena R. Gerstner, Timothy J. Barnette, Andrew J. Tweedell, Craig R. Kleinberg

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2016
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsSubcutaneous fatComposition (language)UltrasoundQuality (philosophy)Internal medicineMedicineAnimal scienceAdipose tissueBiologyPhysicsRadiology

Abstract

fetched live from OpenAlex

Ultrasound echo intensity (EI) values are a popular assessment of muscle quality. The relationship between EI and total (%fat) and regional (%fatlimb) body composition was examined in 40 men, prior to and after accounting for subcutaneous fat thickness. Uncorrected EI values suggest that muscle quality improves (r = –0.329 to –0.224; P = 0.038–0.165) with greater %fat and %fatlimb. However, corrected EI values indicated that muscle quality decreases (r = 0.711 to 0.798; P < 0.001) with greater %fat and %fatlimb.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
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)0.0020.000

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 head0.037
GPT teacher head0.293
Teacher spread0.257 · 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 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

Citations79
Published2016
Admission routes1
Has abstractyes

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