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Record W4401106332 · doi:10.1139/apnm-2024-0046

DXA-derived abdominal fat-free mass to predict MRI skeletal muscle mass in postmenopausal women

2024· article· en· W4401106332 on OpenAlexvenueno aff
Emma Walker, Arushi M Chalke, Victoria L. Bland, Kimberly E. Lind, Robert M. Blew, Andrew Odegaard, Cynthia A. Thomson, Bette J. Caan, J. Skye Nicholas, Celina I. Valencia, Denise J. Roe, Matthew Allison, Peter F. Schnatz, Jennifer W. Bea

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2024
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
FundersNational Cancer InstituteNational Heart, Lung, and Blood InstituteNational Institute on Aging
KeywordsMedicineMagnetic resonance imagingFat free massNuclear medicineDual-energy X-ray absorptiometryGold standard (test)Abdominal fatBody mass indexRadiologyFat massInternal medicineBone mineralObesityOsteoporosis

Abstract

fetched live from OpenAlex

Dual-energy X-ray absorptiometry (DXA) is more available than gold-standard magnetic resonance imaging (MRI), but DXA ability to estimate abdominal skeletal muscle mass (SMM) is unknown. DXA-derived abdominal fat-free mass (FFM; Hologic QDR2000 or QDR4500w) was correlated with single-slice MRI SMM at L4 ( N = 69; r QDR2000 = 0.71, QDR4500w = 0.69; p < 0.0001). Linear regression to predict SMM, including DXA FFM, BMI, and age, resulted in an R-squared of 0.72 and 0.65 for QDR2000 and QDR4500. Bland-Altman limits of agreement were ±21 and ±31 g for 2–3 standard deviations from the mean difference. DXA predicted abdominal SSM is a moderate proxy for MRI abdominal SMM.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.278
Teacher spread0.265 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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