When leveraged together, variable-centered and person-centered statistical methods have the potential to illuminate the factors predicting mental health recovery. However, because extant studies have largely relied on only one of these methods, we do not yet understand why some youth demonstrate recovery while others experience chronic symptoms. This omission limits our understanding of trajectories of physical aggression (AGG) in particular, which are frequently characterized by desistance. The present study examined the development of AGG across childhood and adolescence via variable-centered and person-centered modeling, with neighborhood and family characteristics considered as predictors. Variable-centered results indicated a mean-level decline in AGG with age but were more useful for illuminating predictors of AGG at baseline than predictors of declining engagement. Person-centered analyses, by contrast, identified low parent-child conflict and high household income as predictors of desistance. Although variable-centered analyses were integral to modeling the average AGG trajectory and identifying predictors of engagement at baseline, person-centered techniques proved more useful for understanding predictors of desistance.

Aims: The aim of this audit was to assess adherence to antipsychotic prescribing guidelines in one of the Older Adults Female wards at Kent and Medway NHS and Social Care Partnership Trust (KMPT). Specifically, the audit focused on the documentation of key parameters for patients prescribed antipsychotics, including metabolic and cardiovascular risks, baseline assessments, and monitoring, in accordance with NICE guidelines. A re-audit was conducted in June–July 2024 to evaluate improvements following initial interventions. The original audit was published in BJPsych and presented at RCPsych 2024.

Methods: The initial audit in 2023 identified areas for improvement in documentation and monitoring of antipsychotic prescribing. Key parameters were assessed based on NICE guidelines. An action plan was developed and implemented, including refinements to the existing ward round process and the introduction of a physical health monitoring poster for junior doctors. A re-audit was conducted in 2024, analysing compliance with these parameters for two months of patient records. Compliance rates from 2023 and 2024 were compared to evaluate the effectiveness of the implemented changes.

Results: The re-audit showed significant improvements in several areas. In 2024, compliance for documenting patient age, diagnoses, and MHA status remained at 100%. The documentation of antipsychotic indication improved from 80% in 2023 to 100%. Consent to treatment and the MCA tab improved from 66.66% and 53.33% to 100%. Baseline ECG compliance rose from 86.66% to 100% and repeat ECGs within the recommended time frame increased from 53.33% to 79.17%. Blood tests showed significant improvements: fasting glucose/HbA1c (73.33% to 88%), lipid profile (73.33% to 92%), and liver function (73.33% to 100%). Repeat blood tests, such as fasting glucose, HbA1c, and lipid profile, also showed notable increases. Monitoring for side effects improved to 100%, compared with 46.66% in 2023. GP follow-up recommendations for physical health monitoring were fully documented in 2024, compared with 50% in 2023.

Conclusion: The re-audit demonstrated significant improvements in adherence to antipsychotic prescribing guidelines in one of the Older Adults Female wards at KMPT, particularly in areas related to baseline assessments, monitoring, and follow-up care, all in line with NICE guidelines. The changes made in response to the original audit, including refinements to the existing ward round process and the introduction of a physical health monitoring poster, while accommodating the rotation of junior doctors, were effective in enhancing documentation and compliance. Continued monitoring and future audits will be essential to sustain these improvements and further refine practice and documentation.

Objectives/Goals: Magnetic resonance imaging (MRI) reports are stored as unstructured text in the electronic health record (EHR), rendering the data inaccessible. Large language models (LLM) are a new tool for analyzing and generating unstructured text. We aimed to evaluate how well an LLM extracts data from MRI reports compared to manually abstracted data. Methods/Study Population: The University of California, San Francisco has deployed a HIPAA-compliant internal LLM tool utilizing GPT-4 technology and approved for PHI use. We developed a detailed prompt instructing the LLM to extract data elements from prostate MRI reports and to output the results in a structured, computer-readable format. A data pipeline was built using the OpenAI Application Programming Interface (API) to automatically extract distinct data elements from the MRI report that are important in prostate cancer care. Each prompt was executed five times and data were compared with the modal responses to determine variability of responses. Accuracy was also assessed. Results/Anticipated Results: Across 424 prostate MRI reports, GPT-4 response accuracy was consistently above 95% for most parameters. Individual field accuracies were 98.3% (96.3–99.3%) for PSA density, 97.4% (95.4–98.7%) for extracapsular extension, 98.1% (96.3–99.2%) for TNM Stage, had an overall median of 98.1% (96.3–99.2%), a mean of 97.2% (95.2–98.3%), and a range of 99.8% (98.7–100.0%) to 87.7% (84.2–90.7%). Response variability over five repeated runs ranged from 0.14% to 3.61%, differed based on the data element extracted (p Discussion/Significance of Impact: GPT-4 was highly accurate in extracting data points from prostate cancer MRI reports with low upfront programming requirements. This represents an effective tool to expedite medical data extraction for clinical and research use cases.

Since 2021, 18 states in the USA have restricted education on race and structural inequality. Conservative coalitions frame these restrictions as a war on “woke” ideologies. Through interviews with youth and educators in locales (Florida; Georgia; and York, Pennsylvania) that restrict education on race and structural inequality, I investigate the following: What discourses do students and educators use to describe bills that restrict race-related studies? What, if anything, do their discourses suggest about the perceived political implications of these restrictions?

In this study, I argue that gleaning students’ and educators’ views on “anti-woke” legislation sheds light on the perceived political consequences of these bills for American democracy. I find that students and educators perceive restrictions on race-related studies as epistemic injustices that divest society of the knowledge to identify, problematize, and redress the structural conditions that (re)produce racial subordination. For participants, the health of democracy is contingent on addressing racial disempowerment. Hence, they suggest that restrictions on race-related studies encumber democracy precisely because these policies impose epistemologies of racial ignorance that impede racial redress and allow systems of racial inequality to fester.

A least squares method is presented for fitting a given matrix A to another given matrix B under choice of an unknown rotation, an unknown translation, and an unknown central dilation. The procedure may be useful to investigators who wish to compare results obtained with nonmetric scaling techniques across samples or who wish to compare such results with those obtained by conventional factor analytic techniques on the same sample.

Rapid computational routines are presented for calculating x2 from frequency data in the following cases: (1) test of goodness of fit between an observed and a theoretical distribution; (2) test of independence of distributions displayed in an r ✗ c table; (3) test of independence of distributions displayed in an r ✗ 2 table. A rapid method of computing the contingency coefficient also follows from the procedure used in the second of these cases.

Bootstrap and jackknife techniques are used to estimate ellipsoidal confidence regions of group stimulus points derived from INDSCAL. The validity of these estimates is assessed through Monte Carlo analysis. Asymptotic estimates of confidence regions based on a MULTISCALE solution are also evaluated. Our findings suggest that the bootstrap and jackknife techniques may be used to provide statements regarding the accuracy of the relative locations of points in space. Our findings also suggest that MULTISCALE asymptotic estimates of confidence regions based on small samples provide an optimistic view of the actual statistical reliability of the solution.