Evidence map›Paper›PMID 33159092›Full record

ArticleNPJ schizophrenia2020

Extending schizophrenia diagnostic model to predict schizotypy in first-degree relatives.

Sunil Vasu Kalmady, Animesh Kumar Paul, Russell Greiner, Rimjhim Agrawal, Anekal C Amaresha, Venkataram Shivakumar, Janardhanan C Narayanaswamy, Andrew J Greenshaw, Serdar M Dursun, Ganesan Venkatasubramanian

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ schizophrenia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 43% 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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Review
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

10 authors at 3 institutions in 2 countries.

Sunil Vasu KalmadyAlberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada. kalmady@ualberta.ca.ORCID http://orcid.org/0000-0002-4876-9121
Animesh Kumar PaulAlberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada.
Russell GreinerAlberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada.
Rimjhim AgrawalTranslational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, India.
Anekal C AmareshaDepartment of Sociology and Social Work, Christ- Deemed to be University Bangalore, Bangalore, India.
Venkataram ShivakumarSchizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India.
Janardhanan C NarayanaswamyTranslational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, India.
Andrew J GreenshawDepartment of Psychiatry, University of Alberta, Edmonton, AB, Canada.ORCID http://orcid.org/0000-0002-9097-900X
Serdar M DursunDepartment of Psychiatry, University of Alberta, Edmonton, AB, Canada.
Ganesan VenkatasubramanianTranslational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, India. gvs@nimhans.ac.in.
University of Alberta · CANational Institute of Mental Health and Neurosciences · INChrist University · IN

Funding

Wellcome Trust
6 · The paper itself

Abstract

Recently, we developed a machine-learning algorithm "EMPaSchiz" that learns, from a training set of schizophrenia patients and healthy individuals, a model that predicts if a novel individual has schizophrenia, based on features extracted from his/her resting-state functional magnetic resonance imaging. In this study, we apply this learned model to first-degree relatives of schizophrenia patients, who were found to not have active psychosis or schizophrenia. We observe that the participants that this model classified as schizophrenia patients had significantly higher "schizotypal personality scores" than those who were not. Further, the "EMPaSchiz probability score" for schizophrenia status was significantly correlated with schizotypal personality score. This demonstrates the potential of machine-learned diagnostic models to predict state-independent vulnerability, even when symptoms do not meet the full criteria for clinical diagnosis.

Identifiers

PMID33159092
PMCPMC7648110
OpenAlexW3095290945

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.