Evidence map›Paper›PMID 34718864›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2022

Use of machine learning to select texture features in investigating the effects of axial loading on T

Vahid Abdollah, Eric C Parent, Samin Dolatabadi, Erica Marr, Keith Wachowicz, Michele Battié

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In one paragraph

Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Vahid AbdollahDepartment of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, 2-50 Corbett Hall, Edmonton, AB, T6G2G4, Canada.
Eric C ParentDepartment of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, 2-50 Corbett Hall, Edmonton, AB, T6G2G4, Canada. eparent@ualberta.ca.
Samin DolatabadiFaculty of Science, University of Alberta, 32 Athabasca Hall, Edmonton, AB, T6G 2E8, Canada.
Erica MarrFaculty of Science, University of Alberta, 32 Athabasca Hall, Edmonton, AB, T6G 2E8, Canada.
Keith WachowiczDepartment of Oncology, Medical Physics Division, University of Alberta, 11560, Edmonton, AB, T6G 1Z2, Canada.
Michele BattiéWestern Research Chair in Musculoskeletal Exercise, Mobility and Health, School of Physical Therapy and Western's Bone & Joint Institute, Western University, London, ON, N6A 3K7, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecent advances in texture analysis and machine learning offer new opportunities to improve the application of imaging to intervertebral disc biomechanics. This study employed texture analysis and machine learning on MRIs to investigate the lumbar disc's response to loading.

methodsThirty-five volunteers (30 (SD 11) yrs.) with and without chronic back pain spent 20 min lying in a relaxed unloaded supine position, followed by 20 min loaded in compression, and then 20 min with traction applied. T

findingsAll statistically significant differences (p < 0.001) were observed in the nucleus and posterior annulus in the 135° offset direction at the L4-5 level between lumbar compression and traction. Correlation (P

interpretationMRI textural features may have the potential of identifying the disc's response to loading, particularly in the nucleus and posterior annulus, which appear most sensitive to loading. LEVEL OF EVIDENCE: Diagnostic: individual cross-sectional studies with consistently applied reference standard and blinding.

Indexed as

Intervertebral DiscLumbar VertebraeCross-Sectional StudiesHumansMachine LearningMagnetic Resonance ImagingWeight-BearingCompressionIntervertebral discMachine learningTexture analysisTraction

Identifiers

PMID34718864

What OpenQuestion holds

Textmetadata
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.