Opportunities and Risks of Large Language Models in Psychiatry.

The integration of Large Language Models (LLMs) into mental healthcare and research heralds a potentially transformative shift, one offering enhanced access to care, efficient data collection, and innovative therapeutic tools. This paper reviews the development, function, and burgeoning use of LLMs in psychiatry, highlighting their potential to enhance mental healthcare through improved diagnostic accuracy, personalized care, and streamlined administrative processes. It is also acknowledged that LLMs introduce challenges related to computational demands, potential for misinterpretation, and ethical concerns, necessitating the development of pragmatic frameworks to ensure their safe deployment.

Development of a Practical Prediction Model for Adverse Neonatal Outcomes at the Start of the Second Stage of Labor.

Objective: To develop a prediction model for adverse neonatal outcomes using electronic fetal monitoring (EFM) interpretation data and other relevant clinical information known at the start of the second stage of labor.

Methods: This was a retrospective cohort study of individuals who labored and delivered at two academic medical centers between July 2016 and June 2020. Individuals were included if they had a singleton gestation at term (more than 37 weeks of gestation), a vertex-presenting, nonanomalous fetus, and planned vaginal delivery and reached the start of the second stage of labor.

Advancing Mental Health Research Through Strategic Integration of Transdiagnostic Dimensions and Genomics.

Genome wide studies are yielding a growing catalogue of common and rare variants that confer risk for psychopathology. Yet, despite representing unprecedented progress, emerging data also indicate that the full promise of psychiatric genetics - including understanding pathophysiology and improving personalized care - will not be fully realized by targeting traditional, dichotomous diagnostic categories. The current article provides reflections on themes emerging from a 2021 NIMH sponsored conference convened to address strategies for the evolving field of psychiatric genetics.

A digital intervention for cognitive deficits following COVID-19: a randomized clinical trial.

Post-COVID-19 cognitive deficits are common, persistent, and disabling. Evidence on effective treatments is limited. The goal of this study was to investigate the efficacy of a digital intervention to reduce cognitive and functional deficits in adults with persistent post-COVID-19 cognitive dysfunction.

Conspiratorial thinking in a 50-state survey of American adults.

Background: While the NIMH Research Domain Criteria framework stresses understanding how neuropsychiatric phenotypes vary across populations, little is known outside of small clinical cohorts about conspiratorial thoughts as an aspect of cognition.

Methods: We conducted a 50-state non-probability internet survey conducted in 6 waves between October 6, 2022 and January 29, 2024, with respondents age 18 and older. Respondents completed the American Conspiratorial Thinking Scale (ACTS) and the 9-item Patient Health Questionnaire (PHQ-9).

Trust in Physicians and Hospitals During the COVID-19 Pandemic in a 50-State Survey of US Adults.

Importance: Trust in physicians and hospitals has been associated with achieving public health goals, but the increasing politicization of public health policies during the COVID-19 pandemic may have adversely affected such trust.

Objective: To characterize changes in US adults' trust in physicians and hospitals over the course of the COVID-19 pandemic and the association between this trust and health-related behaviors.

Design, Setting, And Participants: This survey study uses data from 24 waves of a nonprobability internet survey conducted between April 1, 2020, and January 31, 2024, among 443 455 unique respondents aged 18 years or older residing in the US, with state-level representative quotas for race and ethnicity, age, and gender.

Research Letter: Characterizing spin in psychiatric clinical research literature using large language models.

Importance: Spin is a common form of biased reporting that misrepresents study results in publications as more positive than an objective assessment would indicate, but its prevalence in psychiatric journals is unknown.

Objective: To apply a large language model to characterize the extent to which original reports of pharmacologic and non-pharmacologic interventions in psychiatric journals reflect spin.

Design: We identified abstracts from studies published between 2013 and 2023 in 3 high-impact psychiatric journals describing randomized trials or meta-analyses of interventions.

Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians.

Importance: While abundant work has examined patient-level differences in antidepressant treatment outcomes, little is known about the extent of clinician-level differences. Understanding these differences may be important in the development of risk models, precision treatment strategies, and more efficient systems of care.

Objective: To characterize differences between outpatient clinicians in treatment selection and outcomes for their patients diagnosed with major depressive disorder across academic medical centers, community hospitals, and affiliated clinics.

Dimensional Measures of Psychopathology in Children and Adolescents Using Large Language Models.

Background: To enable greater use of National Institute of Mental Health Research Domain Criteria (RDoC) in real-world settings, we applied large language models (LLMs) to estimate dimensional psychopathology from narrative clinical notes.

Methods: We conducted a cohort study using health records from individuals age ≤18 years evaluated in the psychiatric emergency department of a large academic medical center between November 2008 and March 2015. Outcomes were hospital admission and length of emergency department stay.

Stratifying Risk for Postpartum Depression at Time of Hospital Discharge.

Objective: Postpartum depression (PPD) represents a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratification models could enable more targeted interventions in settings with limited resources. Thus, we aimed to develop and estimate the performance of a generalizable risk stratification model for PPD in patients without a history of depression using information collected as part of routine clinical care.

Hofbauer cells and fetal brain microglia share transcriptional profiles and responses to maternal diet-induced obesity.

Maternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many mediated by in utero microglial programming. As microglia remain inaccessible throughout development, identification of noninvasive biomarkers reflecting fetal brain microglial programming could permit screening and intervention. We used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells) in a mouse model of maternal diet-induced obesity, and single-cell RNA-seq to demonstrate shared transcriptional programs.