Evidence map›Paper›PMID 25302313›Full record

Article... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging2013

Balancing Clinical and Pathologic Relevence in the Machine Learning Diagnosis of Epilepsy.

Wesley T Kerr, Andrew Y Cho, Ariana Anderson, Pamela K Douglas, Edward P Lau, Eric S Hwang, Kaavya R Raman, Aaron Trefler, Mark S Cohen, Stefan T Nguyen and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.2field-weighted citation impact, top 46% of its field
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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation.... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging · 2014
    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

12 authors at 1 institution in 1 country.

Wesley T KerrLaboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
Andrew Y ChoLaboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
Ariana AndersonLaboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
Pamela K DouglasLaboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
Edward P LauLaboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
Eric S HwangLaboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
Kaavya R RamanLaboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
Aaron TreflerLaboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
Mark S CohenLaboratory of Integrative Neuroimaging Technology University of California, Los Angeles Los Angeles, USA.
Stefan T NguyenAhmanson Translational Imaging Division University of California, Los Angeles Los Angeles, USA.
Navya M ReddyAhmanson Translational Imaging Division University of California, Los Angeles Los Angeles, USA.
Daniel H SilvermanAhmanson Translational Imaging Division University of California, Los Angeles Los Angeles, USA.
University of California, Los Angeles · US

Funding

UCLA-Caltech Medical Scientist Training ProgramT32GM008042 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AJIJOLA, OLUJIMI A, DAWSON, DAVID WAYNE · 1985 to 2023
$29.9M
Systems in Integrative BiologyT32GM008185 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHOU, TOM · 1987 to 2022
$4.6M
Comprehensive Training in Neuroimaging Fundamentals and ApplicationsR90DA023422 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI COHEN, MARK STEVEN · 2006 to 2015
$1.7M
Real-Time Automated Detection of Craving States with fMRI and EEGR33DA026109 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI COHEN, MARK STEVEN · 2010 to 2012
$1.7M
Comprehensive Training in Neuroimaging Fundamentals and ApplicationsT90DA022768 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI COHEN, MARK STEVEN · 2006 to 2015
$1.5M
NIDA NIH HHS R33 DA026109NIDA NIH HHS R90 DA023422NIDA NIH HHS T90 DA022768NIGMS NIH HHS T32 GM008042NIGMS NIH HHS T32 GM008185
6 · The paper itself

Abstract

The application of machine learning to epilepsy can be used both to develop clinically useful computer-aided diagnostic tools, and to reveal pathologically relevant insights into the disease. Such studies most frequently use neurologically normal patients as the control group to maximize the pathologic insight yielded from the model. This practice yields potentially inflated accuracy because the groups are quite dissimilar. A few manuscripts, however, opt to mimic the clinical comparison of epilepsy to non-epileptic seizures, an approach we believe to be more clinically realistic. In this manuscript, we describe the relative merits of each control group. We demonstrate that in our clinical quality FDG-PET database the performance achieved was similar using each control group. Based on these results, we find that the choice of control group likely does not hinder the reported performance. We argue that clinically applicable computer-aided diagnostic tools for epilepsy must directly address the clinical challenge of distinguishing patients with epilepsy from those with non-epileptic seizures.

Indexed as

controlsepilepsyFDG-PETmachine learningneuroimagingnon-epileptic seizures

Identifiers

PMID25302313
PMCPMC4188528
OpenAlexW1973197311

What OpenQuestion holds

Textmetadata
LicenceTDM
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.