Evidence map›Paper›PMID 39276168›Full record

ReviewCurrent osteoporosis reports2024

Advancements in Subchondral Bone Biomechanics: Insights from Computed Tomography and Micro-Computed Tomography Imaging in Equine Models.

Fatemeh Malekipour, R Chris Whitton, Peter Vee-Sin Lee

Abstract readReview
In one paragraph

Review in Current osteoporosis reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Fatemeh MalekipourDepartment of Biomedical Engineering, University of Melbourne, Parkville, VIC, 3010, Australia. fmal@unimelb.edu.au.
R Chris WhittonEquine Centre, Department of Veterinary Clinical Sciences, University of Melbourne, Werribee, VIC, 3030, Australia.
Peter Vee-Sin LeeDepartment of Biomedical Engineering, University of Melbourne, Parkville, VIC, 3010, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewThis review synthesizes recent advancements in understanding subchondral bone (SCB) biomechanics using computed tomography (CT) and micro-computed tomography (micro-CT) imaging in large animal models, particularly horses. RECENT

findingsRecent studies highlight the complexity of SCB biomechanics, revealing variability in density, microstructure, and biomechanical properties across the depth of SCB from the joint surface, as well as at different joint locations. Early SCB abnormalities have been identified as predictive markers for both osteoarthritis (OA) and stress fractures. The development of standing CT systems has improved the practicality and accuracy of live animal imaging, aiding early diagnosis of SCB pathologies. While imaging advancements have enhanced our understanding of SCB, further research is required to elucidate the underlying mechanisms of joint disease and articular surface failure. Combining imaging with mechanical testing, computational modelling, and artificial intelligence (AI) promises earlier detection and better management of joint disease. Future research should refine these modalities and integrate them into clinical practice to enhance joint health outcomes in veterinary and human medicine.

Indexed as

OsteoarthritisTomography, X-Ray ComputedX-Ray MicrotomographyAnimalsBiomechanical PhenomenaBone and BonesDisease Models, AnimalFractures, StressHorsesJointsComputed tomography imagingEquine joint biomechanicsMicro-computed tomography imagingOsteoarthritis; subchondral bone in stress fractureSubchondral bone microstructureSubchondral bone (SCB)

Identifiers

PMID39276168
PMCPMC11499365

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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 OpenQuestion graph.