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Record W4308518892 · doi:10.1002/jbm4.10696

Greater <scp>pQCT</scp> Calf Muscle Density Is Associated with Lower Fracture Risk, Independent of <scp>FRAX</scp>, Falls and <scp>BMD</scp>: A Meta‐Analysis in the Osteoporotic Fractures in Men (<scp>MrOS</scp>) Study

2022· article· en· W4308518892 on OpenAlexfundno aff
Nicholas C. Harvey, Eric Orwoll, Jane A. Cauley, Timothy Kwok, Magnus K. Karlsson, Björn E. Rosengren, Eva Ribom, Peggy M. Cawthon, Kristine E. Ensrud, Enwu Liu, Faidra Laskou, Kate A. Ward, Elaine Dennison, Cyrus Cooper, John А. Kanis, Liesbeth Vandenput, Mattias Lorentzon, Claes Ohlsson, Dan Mellström, Helena Johansson, Eugène McCloskey

Bibliographic record

VenueJBMR Plus · 2022
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsnot available
FundersMedical Research CouncilNational Institute of Arthritis and Musculoskeletal and Skin DiseasesForskningsrådet om Hälsa, Arbetsliv och VälfärdNational Institute on AgingNational Institute for Health Research Southampton Biomedical Research CentreNovo Nordisk FondenMedical Research Council CanadaNational Institutes of HealthVetenskapsrådetMedical Research FoundationNational Institute for Health and Care ResearchNational Center for Advancing Translational SciencesASCRS Research Foundation
KeywordsFRAXMedicineHazard ratioFemoral neckConfidence intervalBone mineralQuantitative computed tomographyInternal medicineOsteoporosisHip fractureBone densityProportional hazards modelUrologyOsteoporotic fracture

Abstract

fetched live from OpenAlex

ABSTRACT We investigated the predictive performance of peripheral quantitative computed tomography (pQCT) measures of both calf muscle density (an established surrogate for muscle adiposity, with higher values indicating lower muscle adiposity and higher muscle quality) and size (cross‐sectional area [CSA]) for incident fracture. pQCT (Stratec XCT2000/3000) measurements at the tibia were undertaken in Osteoporotic Fractures in Men (MrOS) United States (US), Hong Kong (HK), and Swedish (SW) cohorts. Analyses were by cohort and synthesized by meta‐analysis. The predictive value for incident fracture outcomes, illustrated here for hip fracture (HF), using an extension of Poisson regression adjusted for age and follow‐up time, was expressed as hazard ratio (HR) per standard deviation (SD) increase in exposure (HR/SD). Further analyses adjusted for femoral neck (fn) bone mineral density (BMD) T‐score, Fracture Risk Assessment Tool (FRAX) 10‐year fracture probability (major osteoporotic fracture) and prior falls. We studied 991 (US), 1662 (HK), and 1521 (SW) men, mean ± SD age 77.0 ± 5.1, 73.9 ± 4.9, 80 ± 3.4 years, followed for a mean ± SD 7.8 ± 2.2, 8.1 ± 2.3, 5.3 ± 2.0 years, with 31, 47, and 78 incident HFs, respectively. Both greater muscle CSA and greater muscle density were associated with a lower risk of incident HF [HR/SD: 0.84; 95% confidence interval [CI], 0.72–1.0 and 0.78; 95% CI, 0.66–0.91, respectively]. The pattern of associations was not materially changed by adjustment for prior falls or FRAX probability. In contrast, after inclusion of fn BMD T‐score, the association for muscle CSA was no longer apparent (1.04; 95% CI, 0.88–1.24), whereas that for muscle density was not materially changed (0.69; 95% CI, 0.59–0.82). Findings were similar for osteoporotic fractures. pQCT measures of greater calf muscle density and CSA were both associated with lower incidence of fractures in older men, but only muscle density remained an independent risk factor for fracture after accounting for fn BMD. These findings demonstrate a complex interplay between measures of bone, muscle size, and quality, in determining fracture risk. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.008
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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 designMeta-analysis
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

Citations9
Published2022
Admission routes1
Has abstractyes

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