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The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year-old Danes

Overview of attention for article published in BMC Musculoskeletal Disorders, February 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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18 X users
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6 Facebook pages
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1 Google+ user

Citations

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15 Dimensions

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59 Mendeley
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Title
The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year-old Danes
Published in
BMC Musculoskeletal Disorders, February 2018
DOI 10.1186/s12891-018-1978-x
Pubmed ID
Authors

Rikke K. Jensen, Peter Kent, Tue S. Jensen, Per Kjaer

Abstract

Research into the clinical importance of spinal MRI findings in patients with low back pain (LBP) has primarily focused on single imaging findings, such as Modic changes or disc degeneration, and found only weak associations with the presence of pain. However, numerous MRI findings almost always co-exist in the lumbar spine and are often present at more than one lumbar level. It is possible that multiple MRI findings are more strongly associated with LBP than single MRI findings. Latent Class Analysis is a statistical method that has recently been tested and found useful for identifying latent classes (subgroups) of MRI findings within multivariable datasets. The purpose of this study was to investigate the association between subgroups of MRI findings and the presence of LBP in people from the general population. To identify subgroups of lumbar MRI findings with potential clinical relevance, Latent Class Analysis was initially performed on a clinical dataset of 631 patients seeking care for LBP. Subsequently, 412 participants in a general population cohort (the 'Backs on Funen' project) were statistically allocated to those existing subgroups by Latent Class Analysis, matching their MRI findings at a segmental level. The subgroups containing MRI findings from the general population were then organised into hypothetical pathways of degeneration and the association between subgroups in the pathways and the presence of LBP was tested using exact logistic regression. Six subgroups were identified in the clinical dataset and the data from the general population cohort fitted the subgroups well, with a median posterior probability of 93%-100%. These six subgroups described two pathways of increasing degeneration on upper (L1-L3) and lower (L4-L5) lumbar levels. An association with LBP was found for the subgroups describing severe and multiple degenerative MRI findings at the lower lumbar levels but none of the other subgroups were associated with LBP. Although MRI findings are common in asymptomatic people and the association between single MRI findings and LBP is often weak, our results suggest that subgroups of multiple and severe lumbar MRI findings have a stronger association with LBP than those with milder degrees of degeneration.

X Demographics

X Demographics

The data shown below were collected from the profiles of 18 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 22%
Other 8 14%
Researcher 4 7%
Professor 4 7%
Student > Doctoral Student 3 5%
Other 12 20%
Unknown 15 25%
Readers by discipline Count As %
Medicine and Dentistry 17 29%
Nursing and Health Professions 9 15%
Neuroscience 4 7%
Sports and Recreations 3 5%
Business, Management and Accounting 1 2%
Other 4 7%
Unknown 21 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 July 2020.
All research outputs
#2,492,034
of 23,023,224 outputs
Outputs from BMC Musculoskeletal Disorders
#504
of 4,095 outputs
Outputs of similar age
#55,610
of 331,055 outputs
Outputs of similar age from BMC Musculoskeletal Disorders
#9
of 60 outputs
Altmetric has tracked 23,023,224 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,095 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,055 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.