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Identification of menstrual psychosis cases using electronic health records

Published online by Cambridge University Press:  23 June 2025

Thomas J. Reilly*
Affiliation:
Department of Psychiatry, University of Oxford, Oxford, UK Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK South London and Maudsley NHS Foundation Trust, London, UK
Edward Chesney
Affiliation:
South London and Maudsley NHS Foundation Trust, London, UK Department of Addictions, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
Adam Al-Diwani
Affiliation:
Department of Psychiatry, University of Oxford, Oxford, UK
Amelia Jewell
Affiliation:
South London and Maudsley NHS Foundation Trust, London, UK
Alexis E. Cullen
Affiliation:
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
Dominic Oliver
Affiliation:
Department of Psychiatry, University of Oxford, Oxford, UK NIHR Oxford Health Biomedical Research Centre, Oxford, UK
Philip McGuire
Affiliation:
Department of Psychiatry, University of Oxford, Oxford, UK NIHR Oxford Health Biomedical Research Centre, Oxford, UK
*
Correspondence: Thomas J. Reilly. Email: thomas.reilly@psych.ox.ac.uk
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Abstract

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Letter
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal College of Psychiatrists
Figure 0

Fig. 1 Forest plot of the association of symptom domains (left) and individual symptoms (right) with case–control status. An odds ratio >1 indicates a higher proportion in cases; an odds ratio <1 indicates a higher proportion in controls. Black circles indicate statistical significance corrected at a false discovery rate of 5%.

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