10 Lessons Learned in Launching a Division-Wide Measurement-Based Care Initiative.

Mental health problems are a major source of morbidity and mortality for children and adolescents, affecting 15% to 20% of those under 18 years of age in the US.1 Half of all mental health conditions start by age 14 years, although most cases remain undetected and untreated.2 Despite knowing much about mental health conditions affecting children, many speculate that the lack of standardized approaches to patient care contribute to poor outcomes, including substantial diagnostic variation, few remissions, risk for relapse or recidivism, and, ultimately, greater mortality due to an inability to accurately predict who will make a suicide attempt.3-5 Studies support this over-reliance on the "art of medicine" (ie, subjective judgment without use of standardized measures), finding that only 17.9% of psychiatrists and 11.1% of psychologists in the US routinely administer symptom rating scales to their patients, despite studies suggesting that when using clinical judgment alone, mental health providers detect deterioration for only 21.4% of patients.4.

Predicting Surgical Experience After Robotic Nerve-sparing Radical Prostatectomy Simulation Using a Machine Learning-based Multimodal Analysis of Objective Performance Metrics.

INTRODUCTION: Machine learning methods have emerged as objective tools to evaluate operative performance in urological procedures. Our objectives were to establish machine learning-based methods for predicting surgeon caseload for nerve-sparing robot-assisted radical prostatectomy using our validated hydrogel-based simulation platform and identify potential metrics of surgical expertise. METHODS: Video, robotic kinematics, and force sensor data were collected from 35 board-certified urologists at the 2022 AUA conference. Video was annotated for surgical gestures. Objective performance indicators were derived from robotic system kinematic data. Force metrics were calculated from hydrogel model integrated sensors. Data were fitted to 3 supervised machine learning models-logistic regression, support vector machine, and k-nearest neighbors-which were used to predict procedure-specific learning curve proficiency. Recursive feature elimination was used to optimize the best performing model. RESULTS: Logistic regression predicted caseload with the highest AUC score for 5/7 possible data combinations (force, 64%; objective performance indicators + gestures, 94%; objective performance indicators + force, 90%; gestures + force, 93%; objective performance indicators + gestures + force, 94%). Support vector machine predicted the highest AUC score for objective performance indicators (82%) and gestures (94%). Logistic regression with recursive feature elimination was the most effective model reaching 96% AUC in predicting case-specific experience. Most contributory features were identified across all model types. CONCLUSIONS: We have created a machine learning-based algorithm utilizing a novel combination of objective performance indicators, gesture analysis, and integrated force metrics to predict surgical experience, capable of discriminating between surgeons with low or high robot-assisted radical prostatectomy caseload with 96% AUC in a standardized, simulation-based environment.

Sensation Seeking in Children and Adults with Pediatric-Onset Bipolar Disorder.

Bipolar disorder (BD) is one of the most impairing psychiatric illnesses. Those with pediatric-onset BD tend to have worse outcomes; therefore, accurate conceptualization is important for aspects of care, such as tailored treatment interventions. Sensation seeking behaviors may be a window into the psychopathology of pediatric-onset BD. Participants with BD and healthy controls (HC) ages 7-27 completed self-report assessments, including the Sensation Seeking Scale- V (SSS-V). Among the BD group, there was a significant positive correlation between the Disinhibition subscale and age. Analyses indicated that the BD group scored lower on the Thrill and Adventure Seeking subscale but higher on the Disinhibition scale when compared to the HC group. We found that individuals with pediatric-onset BD are more likely to engage in socially risky behaviors. These results are an important step in understanding sensation seeking characteristics in BD youth and improving treatment, ultimately helping individuals live a more stable life.