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Uncovering Capgras delusion using a large-scale medical records database
BACKGROUND: Capgras delusion is scientifically important but most commonly reported as single case studies. Studies analysing large clinical records databases focus on common disorders but none have investigated rare syndromes. AIMS: Identify cases of Capgras delusion and associated psychopathology,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal College of Psychiatrists
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5541249/ https://www.ncbi.nlm.nih.gov/pubmed/28794897 http://dx.doi.org/10.1192/bjpo.bp.117.005041 |
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author | Bell, Vaughan Marshall, Caryl Kanji, Zara Wilkinson, Sam Halligan, Peter Deeley, Quinton |
author_facet | Bell, Vaughan Marshall, Caryl Kanji, Zara Wilkinson, Sam Halligan, Peter Deeley, Quinton |
author_sort | Bell, Vaughan |
collection | PubMed |
description | BACKGROUND: Capgras delusion is scientifically important but most commonly reported as single case studies. Studies analysing large clinical records databases focus on common disorders but none have investigated rare syndromes. AIMS: Identify cases of Capgras delusion and associated psychopathology, demographics, cognitive function and neuropathology in light of existing models. METHOD: Combined computational data extraction and qualitative classification using 250 000 case records from South London and Maudsley Clinical Record Interactive Search (CRIS) database. RESULTS: We identified 84 individuals and extracted diagnosis-matched comparison groups. Capgras was not ‘monothematic’ in the majority of cases. Most cases involved misidentified family members or close partners but others were misidentified in 25% of cases, contrary to dual-route face recognition models. Neuroimaging provided no evidence for predominantly right hemisphere damage. Individuals were ethnically diverse with a range of psychosis spectrum diagnoses. CONCLUSIONS: Capgras is more diverse than current models assume. Identification of rare syndromes complements existing ‘big data’ approaches in psychiatry. DECLARATION OF INTERESTS: V.B. is supported by a Wellcome Trust Seed Award in Science (200589/Z/16/Z) and the UCLH NIHR Biomedical Research Centre. S.W. is supported by a Wellcome Trust Strategic Award (WT098455MA). Q.D. has received a grant from King’s Health Partners. COPYRIGHT AND USAGE: © The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license. |
format | Online Article Text |
id | pubmed-5541249 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | The Royal College of Psychiatrists |
record_format | MEDLINE/PubMed |
spelling | pubmed-55412492017-08-09 Uncovering Capgras delusion using a large-scale medical records database Bell, Vaughan Marshall, Caryl Kanji, Zara Wilkinson, Sam Halligan, Peter Deeley, Quinton BJPsych Open Paper BACKGROUND: Capgras delusion is scientifically important but most commonly reported as single case studies. Studies analysing large clinical records databases focus on common disorders but none have investigated rare syndromes. AIMS: Identify cases of Capgras delusion and associated psychopathology, demographics, cognitive function and neuropathology in light of existing models. METHOD: Combined computational data extraction and qualitative classification using 250 000 case records from South London and Maudsley Clinical Record Interactive Search (CRIS) database. RESULTS: We identified 84 individuals and extracted diagnosis-matched comparison groups. Capgras was not ‘monothematic’ in the majority of cases. Most cases involved misidentified family members or close partners but others were misidentified in 25% of cases, contrary to dual-route face recognition models. Neuroimaging provided no evidence for predominantly right hemisphere damage. Individuals were ethnically diverse with a range of psychosis spectrum diagnoses. CONCLUSIONS: Capgras is more diverse than current models assume. Identification of rare syndromes complements existing ‘big data’ approaches in psychiatry. DECLARATION OF INTERESTS: V.B. is supported by a Wellcome Trust Seed Award in Science (200589/Z/16/Z) and the UCLH NIHR Biomedical Research Centre. S.W. is supported by a Wellcome Trust Strategic Award (WT098455MA). Q.D. has received a grant from King’s Health Partners. COPYRIGHT AND USAGE: © The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license. The Royal College of Psychiatrists 2017-08-03 /pmc/articles/PMC5541249/ /pubmed/28794897 http://dx.doi.org/10.1192/bjpo.bp.117.005041 Text en © 2017 The Royal College of Psychiatrists http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Paper Bell, Vaughan Marshall, Caryl Kanji, Zara Wilkinson, Sam Halligan, Peter Deeley, Quinton Uncovering Capgras delusion using a large-scale medical records database |
title | Uncovering Capgras delusion using a large-scale medical records database |
title_full | Uncovering Capgras delusion using a large-scale medical records database |
title_fullStr | Uncovering Capgras delusion using a large-scale medical records database |
title_full_unstemmed | Uncovering Capgras delusion using a large-scale medical records database |
title_short | Uncovering Capgras delusion using a large-scale medical records database |
title_sort | uncovering capgras delusion using a large-scale medical records database |
topic | Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5541249/ https://www.ncbi.nlm.nih.gov/pubmed/28794897 http://dx.doi.org/10.1192/bjpo.bp.117.005041 |
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