Evidence map›Paper›PMID 37254620›Full record

ArticleJournal of orthopaedic research : official publication of the Orthopaedic Research Society2024

The knee connectome: A novel tool for studying spatiotemporal change in cartilage thickness.

Jennifer Cummings, Kenneth Gao, Vincent Chen, Alejandro Morales Martinez, Claudia Iriondo, Francesco Caliva, Sharmila Majumdar, Valentina Pedoia

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of orthopaedic research : official publication of the Orthopaedic Research Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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.

Jennifer CummingsDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.ORCID 0000-0002-0848-4009
Kenneth GaoDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.ORCID 0000-0002-5975-0127
Vincent ChenDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Alejandro Morales MartinezDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Claudia IriondoDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.ORCID 0000-0001-7646-6059
Francesco CalivaDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Sharmila MajumdarDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
Valentina PedoiaDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.

Funding

Multidimensional MRI-based Non-Euclidean Deep Learning to Study OsteoarthritisR00AR070902 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PEDOIA, VALENTINA · 2018 to 2020
$747k
Deep Learning for Characterizing Knee Joint Degeneration Predicting Progression of Osteoarthritis and Total Knee ReplacementR33AR073552 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MAJUMDAR, SHARMILA, PEDOIA, VALENTINA · 2020 to 2020
$404k
NIAMS NIH HHS R00 AR070902NIAMS NIH HHS R00AR070902NIAMS NIH HHS R33 AR073552NIAMS NIH HHS R33AR073552
6 · The paper itself

Abstract

Cartilage thickness change is a well-documented biomarker of osteoarthritis pathogenesis. However, there is still much to learn about the spatial and temporal patterns of cartilage thickness change in health and disease. In this study, we develop a novel analysis method for elucidating such patterns using a functional connectivity approach. Descriptive statistics are reported for 1186 knees that did not develop osteoarthritis during the 8 years of observation, which we present as a model of cartilage thickness change related to healthy aging. Within the control population, patterns vary greatly between male and female subjects, while body mass index (BMI) has a more moderate impact. Finally, several differences are shown between knees that did and did not develop osteoarthritis. Some but not all significance appears to be accounted for by differences in sex, BMI, and knee alignment. With this work, we present the connectome as a novel tool for studying spatiotemporal dynamics of tissue change.

Indexed as

Cartilage, ArticularConnectomeOsteoarthritis, KneeFemaleHumansKnee JointMagnetic Resonance ImagingMaleanalysis methodscartilage thicknesskneeosteoarthritis

Identifiers

PMID37254620
PMCPMC10687317

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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.