Publications by authors named "Karen OʼConnor"

Background: Ensuring antibiotics are prescribed only when necessary is crucial for maintaining their effectiveness and is a key focus of public health initiatives worldwide. In cases of sinusitis, among the most common reasons for antibiotic prescriptions in children, healthcare providers must distinguish between bacterial and viral causes based on clinical signs and symptoms. However, due to the overlap between symptoms of acute sinusitis and viral upper respiratory infections, antibiotics are often over-prescribed.

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Background: Overweight and obesity are highly prevalent in people with severe mental illness (SMI). Antipsychotic-induced weight gain (AIWG) is one of the most commonly reported and distressing side effects of treatment and people living with SMI place a high value on the avoidance of this side effect. Metformin is the most effective pharmacological intervention studied for the prevention of AIWG yet clear guidelines are lacking and evidence has not translated into practice.

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Children who have a parent with a psychotic disorder present an increased risk of developing psychosis. It is unclear to date, however, what proportion of all psychosis cases in the population are captured by a familial high-risk for psychosis (FHR-P) approach. This is essential information for prevention research and health service planning, as it tells us the total proportion of psychosis cases that this high-risk approach would prevent if an effective intervention were developed.

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Article Synopsis
  • Adverse drug events (AEs) are a major public health issue, contributing to hospitalizations and affecting patients' quality of life, with social media being explored as a potential source for tracking these events.
  • This study investigates how effective social media analysis is for detecting AEs compared to traditional data sources like clinical literature and reporting systems.
  • The research involved reviewing numerous studies that utilized social media for AE detection, assessing methods, data sources, and the relevance of findings in relation to existing data.
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Background: The mpox outbreak resulted in 32,063 cases and 58 deaths in the United States and 95,912 cases worldwide from May 2022 to March 2024 according to the US Centers for Disease Control and Prevention (CDC). Like other disease outbreaks (eg, HIV) with perceived community associations, mpox can create the risk of stigma, exacerbate homophobia, and potentially hinder health care access and social equity. However, the existing literature on mpox has limited representation of the perspective of sexual minority men and gender-diverse (SMMGD) individuals.

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Background: Few studies have examined the associations between pregnancy and birth complications and long-term (>12 months) maternal mental health outcomes.

Objectives: To review the published literature on pregnancy and birth complications and long-term maternal mental health outcomes.

Search Strategy: Systematic search of Cumulative Index to Nursing and Allied Health Literature (CINAHL), Excerpta Medica Database (Embase), PsycInfo®, PubMed® and Web of Science from inception until August 2022.

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Background: There is some evidence of an association between inflammation in the pathogenesis of mental disorders. Soluble urokinase plasminogen activator receptor (suPAR) is a biomarker of chronic inflammation, which provides a more stable index of systemic inflammation than more widely used biomarkers. This review aims to synthesise studies that measured suPAR concentrations in individuals with a psychiatric disorder, to determine if these concentrations are altered in comparison to healthy participants.

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Objectives: To synthesize discussions among sexual minority men and gender diverse (SMMGD) individuals on mpox, given limited representation of SMMGD voices in existing mpox literature.

Methods: BERTopic (a topic modeling technique) was employed with human validations to analyze mpox-related tweets ( = 8,688; October 2020-September 2022) from 2,326 self-identified SMMGD individuals in the U.S.

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Article Synopsis
  • The text discusses the significance of real-world data from social media, particularly Twitter, for health and social science research, emphasizing the need to identify user demographics like age and gender to evaluate research representativeness.
  • It outlines the objective of a scoping review that summarizes existing literature on methods for predicting Twitter users' age and gender, noting the challenges involved in this process.
  • The review analyzed 684 studies, finding 74 relevant ones that discussed age or gender prediction, revealing a predominance in gender prediction methods, with varying levels of performance in accuracy for both age and gender classifications.
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Objective: Goals of care (GOC) discussions are an increasingly used quality metric in serious illness care and research. Wide variation in documentation practices within the Electronic Health Record (EHR) presents challenges for reliable measurement of GOC discussions. Novel natural language processing approaches are needed to capture GOC discussions documented in real-world samples of seriously ill hospitalized patients' EHR notes, a corpus with a very low event prevalence.

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Objective: To examine the association between threatened miscarriage, and neurodevelopmental disorders, including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) in offspring by age 14 years.

Methods: We used data from the Millennium Cohort Study, a nationally representative longitudinal study of children born in the UK. Data on threatened miscarriage and potential confounders were maternal-reported and collected at 9 months postpartum.

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Background And Hypothesis: Recent research showed that young people who presented to hospital with self-harm in Finland had a significantly elevated risk of later psychosis. We investigated the prospective relationship between hospital presentation for self-harm and risk of psychosis in an unprecedentedly large national Swedish cohort.

Study Design: We used inpatient and outpatient healthcare registers to identify all individuals born between 1981 and 1993 who were alive and living in Sweden on their 12th birthday and who presented to hospital one or more times with self-harm.

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Article Synopsis
  • Hypertension is a major global health issue, marked by high rates of medication nonadherence, which traditional surveys struggle to accurately assess due to various biases.
  • The study leveraged patient reviews from WebMD to analyze reasons for changes in angiotensin receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors (ACEIs) using natural language processing.
  • Out of 343,459 reviews, the analysis revealed that a significant majority of users reported adverse events—primarily musculoskeletal issues for ARBs and respiratory problems for ACEIs—as the main reasons for adjusting their medications.
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Background: Existing studies have established an association between pregnancy, birth complications, and mental health in the first few weeks postpartum. However, there is no clear understanding of whether pregnancy and birth complications increase the risk of adverse maternal mental outcomes in the longer term. Research on maternal adverse mental health outcomes following pregnancy and birth complications beyond 12 months postpartum is scarce, and findings are inconsistent.

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Objectives: Xylazine is an α 2 -agonist increasingly prevalent in the illicit drug supply. Our objectives were to curate information about xylazine through social media from people who use drugs (PWUDs). Specifically, we sought to answer the following: (1) What are the demographics of Reddit subscribers reporting exposure to xylazine? (2) Is xylazine a desired additive? And (3) what adverse effects of xylazine are PWUDs experiencing?

Methods: Natural language processing (NLP) was used to identify mentions of "xylazine" from posts by Reddit subscribers who also posted on drug-related subreddits.

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Background: Phenotypes identified during dysmorphology physical examinations are critical to genetic diagnosis and nearly universally documented as free-text in the electronic health record (EHR). Variation in how phenotypes are recorded in free-text makes large-scale computational analysis extremely challenging. Existing natural language processing (NLP) approaches to address phenotype extraction are trained largely on the biomedical literature or on case vignettes rather than actual EHR data.

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Article Synopsis
  • * Natural language processing (NLP) methods can significantly streamline the extraction process, particularly as the COVID-19 pandemic highlighted gaps in essential data, such as demographics and clinical outcomes in genomic records.
  • * The development of automated pipelines using machine learning and NLP will allow for better identification of key patient characteristics from relevant articles, enhancing the richness of data available for epidemiological studies.
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Background: Adverse drug events (ADEs) are a considerable public health burden resulting in disability, hospitalization, and death. Even those ADEs deemed nonserious can severely impact a patient's quality of life and adherence to intervention. Monitoring medication safety, however, is challenging.

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Background: There has been an unprecedented effort to sequence the SARS-CoV-2 virus and examine its molecular evolution. This has been facilitated by the availability of publicly accessible databases, the Global Initiative on Sharing All Influenza Data (GISAID) and GenBank, which collectively hold millions of SARS-CoV-2 sequence records. Genomic epidemiology, however, seeks to go beyond phylogenetic analysis by linking genetic information to patient characteristics and disease outcomes, enabling a comprehensive understanding of transmission dynamics and disease impact.

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  • SSRIs are widely prescribed for mental health issues, but many patients stop taking them, making it crucial to understand why.
  • A study analyzed online drug reviews to find out the reasons for discontinuation, changes, or dose adjustments of SSRIs, using data from 667 reviews on WebMD.
  • The research found that the main reason for stopping or switching SSRIs was adverse side effects, while dose changes were largely due to adjustments by either patients or healthcare professionals.
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  • The introduction of large language models (LLMs) represents a significant change in how we generate text, allowing for human-like chat interactions.
  • LLM-based chatbots can enhance academic efficiency, but ethical issues like fair use and biases need to be addressed.
  • The editorial emphasizes the importance of effective usage, distinguishes between LLM use and plagiarism, calls for addressing bias and accuracy concerns, and highlights a promising future for LLM applications in academia.
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Background And Hypothesis: Intestinal microbiota is intrinsically linked to human health. Evidence suggests that the composition and function of the microbiome differs in those with schizophrenia compared with controls. It is not clear how these alterations functionally impact people with schizophrenia.

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