Publications by authors named "John Geddes"

Cognitive and neural mechanisms underlying bipolar disorder (BD) and its treatment are still poorly understood. Here we examined the role of adaptations in risk-taking using a reward-guided decision-making task. We recruited volunteers with high (n = 40) scores on the Mood Disorder Questionnaire, MDQ, suspected of high risk for bipolar disorder and those with low-risk scores (n = 37).

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Background: Options for 'treatment-resistant bipolar depression' (TRBD) are limited. Two small, short-term, trials of pramipexole suggest it might be an option.

Aims: To evaluate the clinical effectiveness and safety of pramipexole in the management of TRBD.

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Background: Links between personality disorders and antisocial outcomes has not examined individual personality disorders, and the contribution of comorbidities remain uncertain. Previous systematic reviews are dated.

Aims: To synthesise evidence from observational studies on the risk of antisocial outcomes and recidivism associated with personality disorders.

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Objective: To assess whether the antiseizure medication levetiracetam may improve cognition in individuals with Alzheimer's disease who have not previously experienced a seizure.

Methods: We performed a randomized, double-blind, placebo-controlled crossover pilot study in individuals with mild-to-moderate Alzheimer's disease. Electroencephalography was performed at baseline and those with active epileptiform discharges were excluded.

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Background: In patients with coronavirus disease 2019 (COVID-19) requiring supplemental oxygen, dexamethasone reduces acute severity and improves survival, but longer-term effects are unknown. We hypothesised that systemic corticosteroid administration during acute COVID-19 would be associated with improved health-related quality of life (HRQoL) 1 year after discharge.

Methods: Adults admitted to hospital between February 2020 and March 2021 for COVID-19 and meeting current guideline recommendations for dexamethasone treatment were included using two prospective UK cohort studies (Post-hospitalisation COVID-19 and the International Severe Acute Respiratory and emerging Infection Consortium).

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The malicious use of artificial intelligence is growing rapidly, creating major security threats for individuals and the healthcare sector. Individuals with mental illness may be especially vulnerable. Healthcare provider data are a prime target for cybercriminals.

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Background: COVID-19 is known to be associated with increased risks of cognitive and psychiatric outcomes after the acute phase of disease. We aimed to assess whether these symptoms can emerge or persist more than 1 year after hospitalisation for COVID-19, to identify which early aspects of COVID-19 illness predict longer-term symptoms, and to establish how these symptoms relate to occupational functioning.

Methods: The Post-hospitalisation COVID-19 study (PHOSP-COVID) is a prospective, longitudinal cohort study of adults (aged ≥18 years) who were hospitalised with a clinical diagnosis of COVID-19 at participating National Health Service hospitals across the UK.

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Online self-diagnosis of psychiatric disorders by the general public is increasing. The reasons for the increase include the expansion of Internet technologies and the use of social media, the rapid growth of direct-to-consumer e-commerce in healthcare, and the increased emphasis on patient involvement in decision making. The publicity given to artificial intelligence (AI) has also contributed to the increased use of online screening tools by the general public.

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Aims And Method: We aimed to co-design an intervention optimising the benefits of online arts and culture for mental health in young people for subsequent testing in a trial. Co-design followed the double diamond phases of design, discover, define, develop and deliver.

Results: Navigating the views of all co-designers to produce a testable resource demanded in-depth understanding, and frequent iterations in multiple modalities of the theoretical basis of the intervention, amplification of youth voice and commitment to a common goal.

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A proportion of patients infected with severe acute respiratory syndrome coronavirus 2 experience a range of neuropsychiatric symptoms months after infection, including cognitive deficits, depression and anxiety. The mechanisms underpinning such symptoms remain elusive. Recent research has demonstrated that nervous system injury can occur during COVID-19.

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With the recent advances in artificial intelligence (AI), patients are increasingly exposed to misleading medical information. Generative AI models, including large language models such as ChatGPT, create and modify text, images, audio and video information based on training data. Commercial use of generative AI is expanding rapidly and the public will routinely receive messages created by generative AI.

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Background: Participation in clinical research is associated with better patient outcomes and higher staff retention and satisfaction rates. Nevertheless, patient recruitment to mental health studies is challenging due to a reliance on clinician or patient referrals (standard approach). To empower patients and make healthcare research more equitable, we explored a novel researcher-led approach, called 'Count Me In' (CMI).

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Article Synopsis
  • Post-COVID cognitive deficits, often described as 'brain fog', are commonly seen in patients and significantly impair daily functioning, but their exact biological causes are still unclear.
  • A study with 1,837 adults hospitalized due to COVID-19 identified two specific biomarker profiles from their blood tests that can predict cognitive issues 6 to 12 months post-infection.
  • The first profile involves high fibrinogen levels linked to cognitive impairments, while the second profile, marked by elevated D-dimer levels, connects more with subjective cognitive issues and work performance, with fatigue and shortness of breath playing a mediating role.
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This narrative review discusses how the safe and effective use of clinical artificial intelligence (AI) prediction tools requires recognition of the importance of human intelligence. Human intelligence, creativity, situational awareness, and professional knowledge, are required for successful implementation. The implementation of clinical AI prediction tools may change the workflow in medical practice resulting in new challenges and safety implications.

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The time a red blood cell (RBC) spends in the microvasculature is of prime importance for a number of physiological processes. In this work, we present a methodology for computing an approximation of the so-called transit time distribution (TTD), i.e.

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Background: Dopamine D-like agonists show promise as treatments for depression. They are thought to act by enhancing reward learning; however, the mechanisms by which they achieve this are not clear. Reinforcement learning accounts describe 3 distinct candidate mechanisms: increased reward sensitivity, increased inverse decision-temperature, and decreased value decay.

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Purpose Of Review: Telepsychiatry practiced by psychiatrists is evidence-based, regulated, private, and effective in diverse settings. The use of telemedicine has grown since the COVID-19 pandemic as people routinely obtain more healthcare services online. At the same time, there has been a rapid increase in the number of digital mental health startups that offer various services including online therapy and access to prescription medications.

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Background: Growing evidence suggests that community-based interventions may be effective for anxiety and depression. This study aimed to describe studies of community interventions delivered to adults and/or young people, either in person or online, evaluated in randomised controlled trials and provide an indication as to their effectiveness, acceptability, quality of data and where possible, mechanisms of action. We included interventions delivered at and/or by museums, art galleries, libraries, gardens, music groups/choirs and sports clubs.

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Background: User feedback is crucial in the development of electronic self-monitoring tools for bipolar spectrum disorders (BSD). Previous studies have examined user experiences in small samples self-monitoring over relatively short time periods. We aimed to explore the experiences of a large sample of individuals with BSD engaged in long-term remote active electronic self-monitoring.

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Background: Difficult-to-treat depression (DTD) presents a substantial health care challenge, with around one-third of people diagnosed with a depressive episode in the UK finding that their symptoms persist following treatment. This study aimed to identify priority research questions (RQs) that could inform the development of new and improved treatments, interventions, and support for people with DTD.

Methods: Using an adapted Child Health and Nutrition Research Initiative (CHNRI) method, this national prioritisation exercise engaged 60 leading researchers and health care professionals in the UK, as well as 25 wider stakeholders with relevant lived experience to produce a ranked list of priority RQs in DTD.

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