Evidence map›Paper›PMID 37262347›Full record

ArticleArthritis & rheumatology (Hoboken, N.J.)2023

Large-Scale Analysis of Meniscus Morphology as Risk Factor for Knee Osteoarthritis.

Kenneth T Gao, Emily Xie, Vincent Chen, Claudia Iriondo, Francesco Calivà, Richard B Souza, Sharmila Majumdar, Valentina Pedoia

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Arthritis & rheumatology (Hoboken, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
4.0field-weighted citation impact, top 6% of its field
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

10 citing papers in PubMed, 16 citations in OpenAlex.

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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

8 authors at 1 institution in 2 countries.

Kenneth T GaoUniversity of California, San Francisco and University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering, San Francisco, United States.ORCID 0000-0002-5975-0127
Emily XieCenter for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States.
Vincent ChenCenter for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States.
Claudia IriondoUniversity of California, San Francisco and University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering, San Francisco, United States.
Francesco CalivàCenter for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States.
Richard B SouzaCenter for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco and Department of Physical Therapy and Rehabilitation Science, University of California, San Francisco, United States.
Sharmila MajumdarCenter for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States.
Valentina PedoiaCenter for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States.
University of California, San Francisco · US

Funding

Multidimensional MRI-based Non-Euclidean Deep Learning to Study OsteoarthritisR00AR070902 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PEDOIA, VALENTINA · 2018 to 2020
$747k
NIAMS NIH HHS R00 AR070902NIH HHS N01-AR-2-2258NIH HHS N01-AR-2-2259NIH HHS N01-AR-2-2260NIH HHS N01-AR-2-2261NIH HHS N01-AR-2-2262NIH HHS R00-AR-070902
6 · The paper itself

Abstract

objectiveAlthough it is established that structural damage of the meniscus is linked to knee osteoarthritis (OA) progression, the predisposition to future development of OA because of geometric meniscal shapes is plausible and unexplored. This study aims to identify common variations in meniscal shape and determine their relationships to tissue morphology, OA onset, and longitudinal changes in cartilage thickness.

methodsA total of 4,790 participants from the Osteoarthritis Initiative data set were studied. A statistical shape model was developed for the meniscus, and shape scores were evaluated between a control group and an OA incidence group. Shape features were then associated with cartilage thickness changes over 8 years to localize the relationship between meniscus shape and cartilage degeneration.

resultsSeven shape features between the medial and lateral menisci were identified to be different between knees that remain normal and those that develop OA. These include length-width ratios, horn lengths, root attachment angles, and concavity. These "at-risk" shapes were linked to unique cartilage thickness changes that suggest a relationship between meniscus geometry and decreased tibial coverage and rotational imbalances. Additionally, strong associations were found between meniscal shape and demographic subpopulations, future tibial extrusion, and meniscal and ligamentous tears.

conclusionThis automatic method expanded upon known meniscus characteristics that are associated with the onset of OA and discovered novel shape features that have yet to be investigated in the context of OA risk.

Indexed as

Cartilage DiseasesMeniscusOsteoarthritis, KneeHumansMagnetic Resonance ImagingMenisci, TibialRisk Factors

Identifiers

PMID37262347
PMCPMC10706605
OpenAlexW4379051618

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

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.