Evidence map›Paper›PMID 31491827›Full record

Trial reportNeuroImage. Clinical2019

FLAIR

M Le, L Y W Tang, E Hernández-Torres, M Jarrett, T Brosch, L Metz, D K B Li, A Traboulsee, R C Tam, A Rauscher and 1 more

Abstract readClinical TrialMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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

11 authors.

M LeMS/MRI Research Group (Division of Neurology), University of British Columbia, Vancouver, BC, Canada.
L Y W TangMS/MRI Research Group (Division of Neurology), University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada.
E Hernández-TorresDepartment of Pediatrics, University of British Columbia, Vancouver, BC, Canada; UBC MRI Research Centre, University of British Columbia, Vancouver, BC, Canada; Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.
M JarrettDepartment of Pediatrics, University of British Columbia, Vancouver, BC, Canada; Population Data BC, Vancouver, BC, Canada.
T BroschMS/MRI Research Group (Division of Neurology), University of British Columbia, Vancouver, BC, Canada; Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada; Philips Medical Innovative Technologies, Hamburg, Germany.
L MetzDepartment of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.
D K B LiMS/MRI Research Group (Division of Neurology), University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada; UBC MRI Research Centre, University of British Columbia, Vancouver, BC, Canada.
A TraboulseeDepartment of Neurology (Division of Medicine), University of British Columbia, Vancouver, BC, Canada.
R C TamMS/MRI Research Group (Division of Neurology), University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada.
A RauscherDepartment of Pediatrics, University of British Columbia, Vancouver, BC, Canada; BC Children's Hospital Research Institute, Canada; Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.
V WiggermannDepartment of Pediatrics, University of British Columbia, Vancouver, BC, Canada; UBC MRI Research Centre, University of British Columbia, Vancouver, BC, Canada; Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada. Electronic address: vwiggerm@phas.ubc.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccurate segmentation of MS lesions on MRI is difficult and, if performed manually, time consuming. Automatic segmentations rely strongly on the image contrast and signal-to-noise ratio. Literature examining segmentation tool performances in real-world multi-site data acquisition settings is scarce.

objectiveFLAIR

methodsLesions were segmented on 47 MS patient data sets obtained from 34 sites using LesionTOADS, OASIS and LST, and compared to a semi-automatically generated reference. The performance of FLAIR and FLAIR

resultsCompared to FLAIR, the use of FLAIR

conclusionsIn this real-world, multi-center experiment, FLAIR

Indexed as

AdultCohort StudiesFemaleHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedMultiple Sclerosis, Relapsing-RemittingNeuroimagingFLAIRFLAIR(2)Lesion volumeMulti-centerPerformance evaluationSegmentation

Identifiers

PMID31491827
PMCPMC6646743

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.