When is Psychotherapy Brief? Considering Sociodemographic Factors, Problem Complexity, and Problem Type in U.S. Adolescents.

Objective: When psychotherapy is brief (1-2 sessions), "early dropout" - defined as premature treatment discontinuation due to financial or structural barriers - is a commonly assumed cause. However, there are several possible reasons why treatment may be brief, including youth-level factors such as psychopathology complexity or problem type. Better characterizing whether factors beyond financial and structural barriers predict adolescents' receipt of briefer (versus longer-term) treatment may guide efforts to retain specific youth in longer-term services - and disseminate intentionally brief interventions to youth potentially positioned to benefit.Method: Using data from the 2017 SAMHSA National Survey on Drug Use and Health, we examined whether sociodemographic disadvantage (minority race, low-income, government assistance), perceived problem type, and psychopathology complexity (1 versus multiple problem types) related to psychotherapy length (1-2 versus 3-24+ sessions) among adolescents receiving outpatient psychotherapy (N = 1,601; ages 12-17; 60.59% white; 64.50% female).Results: Among adolescents beginning outpatient psychotherapy, 23.36% ended treatment after 1-2 sessions. Psychopathology complexity predicted greater likelihood of receiving >2 sessions, after adjusting for specific problem type (χ2 = 75.14, p < .001, OR = 1.80). Further, although certain problem types (e.g., depression, anxiety, and anger control) were associated with increased likelihood of greater treatment length, these findings did not hold after accounting for psychopathology complexity. No sociodemographic factors significantly predicted treatment length.Conclusions: Structural and financial barriers alone may not explain when and why youth psychotherapy is brief. Additional factors, such as psychopathology complexity, may be important and potentially primary contributors to treatment duration among youth who access outpatient services. Future research may examine whether youth with less comorbidity differentially benefit from intentionally brief interventions, along with strategies for retaining youth who might benefit from longer-term care - such as those with multiple co-occurring problems - in treatment.

Use of machine learning in pediatric surgical clinical prediction tools: A systematic review.

PURPOSE: Clinical prediction tools (CPTs) are decision-making instruments utilizing patient data to predict specific clinical outcomes, risk-stratify patients, or suggest personalized diagnostic or therapeutic options. Recent advancements in artificial intelligence have resulted in a proliferation of CPTs created using machine learning (ML)-yet the clinical applicability of ML-based CPTs and their validation in clinical settings remain unclear. This systematic review aims to compare the validity and clinical efficacy of ML-based to traditional CPTs in pediatric surgery. METHODS: Nine databases were searched from 2000 until July 9, 2021 to retrieve articles reporting on CPTs and ML for pediatric surgical conditions. PRISMA standards were followed, and screening was performed by two independent reviewers in Rayyan, with a third reviewer resolving conflicts. Risk of bias was assessed using the PROBAST. RESULTS: Out of 8300 studies, 48 met the inclusion criteria. The most represented surgical specialties were pediatric general (14), neurosurgery (13) and cardiac surgery (12). Prognostic (26) CPTs were the most represented type of surgical pediatric CPTs followed by diagnostic (10), interventional (9), and risk stratifying (2). One study included a CPT for diagnostic, interventional and prognostic purposes. 81% of studies compared their CPT to ML-based CPTs, statistical CPTs, or the unaided clinician, but lacked external validation and/or evidence of clinical implementation. CONCLUSIONS: While most studies claim significant potential improvements by incorporating ML-based CPTs in pediatric surgical decision-making, both external validation and clinical application remains limited. Further studies must focus on validating existing instruments or developing validated tools, and incorporating them in the clinical workflow. TYPE OF STUDY: Systematic Review LEVEL OF EVIDENCE: Level III.

System Identification and Two-Degree-of-Freedom Control of Nonlinear, Viscoelastic Tissues.

OBJECTIVE: This paper presents a force control scheme for brief isotonic holds in an isometrically contracted muscle tissue, with minimal overshoot and settling time to measure its shortening velocity, a key parameter of muscle function. METHODS: A two-degree-of-freedom control configuration, formed by a feedback controller and a feedforward controller, is explored. The feedback controller is a proportional-integral controller and the feedforward controller is designed using the inverse of a control-oriented model of muscle tissue. A generalized linear model and a nonlinear model of muscle tissue are explored using input-output data and system identification techniques. The force control scheme is tested on equine airway smooth muscle and its robustness confirmed with murine flexor digitorum brevis muscle. RESULTS: Performance and repeatability of the force control scheme as well as the number of inputs and level of supervision required from the user were assessed with a series of experiments. The force control scheme was able to fulfill the stated control objectives in most cases, including the requirements for settling time and overshoot. CONCLUSION: The proposed control scheme is shown to enable automation of force control for characterizing muscle mechanics with minimal user input required. SIGNIFICANCE: This paper leverages an inversion-based feedforward controller based on a nonlinear physiological model in a system identification context that is superior to classic linear system identification. The control scheme can be used as a steppingstone for generalized control of nonlinear, viscoelastic materials.