Evidence map›Paper›PMID 31548477›Full record

ArticleMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2020

Interobserver Reliability When Classifying MR Imaging of the Lumbar Spine: Written Instructions Alone Do Not Suffice.

Ulf Krister Hofmann, Ramona Luise Keller, Marco Gesicki, Christian Walter, Falk Mittag

Abstract read
In one paragraph

Article in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Spine Medical Image Segmentation Based on Deep Learning.Journal of healthcare engineering · 2021
    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

5 authors.

Ulf Krister HofmannDepartment of Orthopaedic Surgery, University Hospital of Tübingen.
Ramona Luise KellerFaculty of Medicine, Julius-Maximilians University of Würzburg.
Marco GesickiDepartment of Orthopaedic Surgery, University Hospital of Tübingen.
Christian WalterDepartment of Orthopaedic Surgery, University Hospital of Tübingen.
Falk MittagDepartment of Orthopaedic Surgery, University Hospital of Tübingen.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeNumerous classification systems have been proposed to analyze lumbar spine MRI scans. When evaluating these systems, most studies draw their conclusions from measurements of experienced clinicians. The aim of this study was to evaluate the impact of specific measurement training on interobserver reliability in MRI classification of the lumbar spine.

methodsVarious measurement and classification systems were assessed for their interobserver reliability in 30 MRIs from patients with chronic lumbar back and sciatic pain. Two observers were experienced spine surgeons. The third observer was an inexperienced medical student who, prior to the study measurements, in addition to being given the detailed written instructions also given to the surgeons, obtained a list of 20 reference measurements in MRI scans from other patients to practice with.

resultsExcellent agreement was observed between the medical student and the spine surgeon who had also created the reference measurements. Between the two spine surgeons, agreement was markedly lower in all systems investigated (e.g., antero-posterior spinal canal diameter intraclass correlation coefficient [ICC] [3.1] = 0.979 vs. ICC [3.1] = 0.857).

conclusionThese data warrant the creation of publicly available standardised measurement examples of accepted classification systems to increase reliability of the interpretation of MR images.

Indexed as

HumansLow Back PainLumbar VertebraeMagnetic Resonance ImagingObserver VariationReproducibility of Resultsfacet joint degenerationlumbar spinal stenosismagnetic resonance imagingneuroforaminal stenosisSchizas’ spinal stenosis classification

Identifiers

PMID31548477
PMCPMC7553807

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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.