Evidence map›Paper›PMID 41915170›Full record

ArticleSkeletal radiology2026

Quantitative MRI of core muscles at different activity levels: muscle-specific metrics and composite core fat fraction and lean-volume scores.

Martin Belzunce, Henry Hardman, Anna Di Laura, Johann Henckel, Alister Hart

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Article in Skeletal radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Martin BelzunceRoyal National Orthopaedic Hospital NHS Trust, Stanmore, UK. martin.belzunce@nhs.net.ORCID http://orcid.org/0000-0001-6085-484X
Henry HardmanDivision of Surgery & Interventional Science, University College London, London, UK.
Anna Di LauraRoyal National Orthopaedic Hospital NHS Trust, Stanmore, UK.
Johann HenckelRoyal National Orthopaedic Hospital NHS Trust, Stanmore, UK.
Alister HartRoyal National Orthopaedic Hospital NHS Trust, Stanmore, UK.ORCID http://orcid.org/0000-0003-1281-6886

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo combine Dixon-based quantitative MRI with automated segmentation to quantify fat fraction (FF) and lean normalised muscle volume (LNV) in seven core muscles and to derive two composite scores, comparing highly active cyclists with a physically inactive group.

methodsCyclists (n = 84) and physically inactive volunteers (n = 85) underwent Dixon MRI of the lumbar spine and pelvis capturing images of the psoas major, iliacus, quadratus lumborum, erector spinae/multifidus, gluteus maximus, gluteus medius and gluteus minimus. Images were analysed using automated segmentation to determine the FF and LNV of each muscle and two composite core scores summarising mean fat fraction (FF

resultsCyclists showed lower FF and higher LNV than physically inactive participants across muscles and for both composite scores. Age was a significant predictor of FF in all muscles and in FF

conclusionsWe identified muscle-specific differences in core composition between highly active cyclists and physically inactive adults, and FF

Indexed as

Adipose TissueMagnetic Resonance ImagingMuscle, SkeletalAdultBicyclingBody CompositionFemaleHumansMaleMiddle AgedAgeingCore musclesFat fractionLean normalised volumeMagnetic resonance imagingMuscle compositionQuantitative MRI

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.