Clinical Solutions

Educational practices have been positively impacted by Generative Artificial Intelligence (AI) and Large Language Models (LLMs) such as ChatGPT. Unlike traditional systems and learning platforms, ChatGPT engages users in conversations, provides tailored feedback, and creates new knowledge in virtually any area. This paper provides a detailed model for educational practice using ChatGPT and other generative AI systems. The model attempts to address, in an equitable manner, the three most challenging aspects of educational practice: pedagogical concerns, technological availability, and ethical peripherals. The model enhances students’ engagement, promotes the development of their higher-order thinking skills, and is designed to improve learning outcomes through personalization. This paper discusses a conceptual model for adaptive generative tutoring and provides technical illustrations, tables, and formulae to describe the process. The use of generative AI in STEM problem solving and language learning is primary, but the challenges of academic honesty, bias, and data privacy are also addressed. The current paper provides a potential framework for the thoughtful and purposeful use of ChatGPT in educational settings and makes a valuable contribution to the discourse on the intersection of educational technology and the ethics of AI in teaching.

This research paper explores how an agentic, Bayesian optimisation driven marketing mix modelling (MMM) framework offers significant advantages over traditional, static regression-based methods, advancing MMM from its econometric roots to better meet the needs of modern marketers. Traditional MMM often relies on linear regression with predefined transformations and fixed decay rates, leading to sub-optimal parameter estimations and limited predictive power. Our proposed agentic approach leverages Bayesian optimisation to dynamically discover optimal adstock (carryover) and diminishing returns (saturation) parameters for each marketing channel. This data-driven, adaptive parameter tuning, combined with robust feature engineering for seasonality and automated model logging and drift detection, results in more accurate sales attribution, improved return on advertising spend (ROAS) predictions and enhanced strategic budget allocation. This paper details the methodological advancements, presents the underlying mathematical formulations, and discusses the practical implications and benefits for marketing practitioners seeking to maximise their marketing effectiveness. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

The relationship between excess body weight and stress significantly impacts overall health. The current study evaluated the efficacy and safety of Ashwagandha (Withania somnifera) root extract (ARE) in altering stress biomarkers and its role in weight management. In this prospective, randomized, double-blind, placebo-controlled study, 100 participants (age: 19-65 years) were randomized to receive either ARE 300 mg twice daily (n = 50) or an identical placebo (PL, n = 50) for 24 weeks. The primary efficacy endpoints were changes in body weight and body mass index (BMI). Secondary endpoints included evaluation of stress (Perceived Stress Scale, PSS), quality of life (Short Form-12 Quality of Life Scale), subjective satisfaction (Subjective Satisfaction Scale, SSS), and food cravings (Food Cravings Questionnaire-Trait, FCQ-T). Clinical safety was assessed based on treatment-emergent adverse events (TEAEs) and laboratory parameters, including complete blood count, renal function tests, liver function tests, lipid profile, thyroid function tests, and glycemic parameters (plasma glucose and glycated hemoglobin). Efficacy was analyzed in 91 patients. ARE administration resulted in a significant reduction in body weight (-8.46 ± 3.86 kg; P < 0.0001) and BMI (-3.31 ± 1.57 kg/m2; P < 0.0001) compared to the PL (-2.41 ± 2.07 kg and -0.93 ± 0.79 kg/m2 for body weight and BMI, respectively). Compared with PL, ARE showed significant improvements (P < 0.05) in PSS, SF-12, SSS, and FCQ-T scores. A total of seven participants with ARE and six with PL reported mild adverse events (nausea, abdominal pain, and drowsiness), which were resolved without any intervention. Ashwagandha root extract may offer a safe and beneficial approach for stress reduction and weight management.

The push to use metallic lithium‐based batteries motivates a shift toward the use of solid polymer electrolytes. To improve the ionic conductivity values of such electrolytes, liquid additives (plasticizers) are usually added. However, the improvement in conductivity comes at the expense of a deterioration of the anode–electrolyte interface, resulting in poorer electrochemical cell performance. In this study, the use of a polymer coating consisting of polyethylene oxide, LiTFSI, and LiNO 3 is proposed. The coating shows improved electrochemical performance and stability, delayed cell failure and a more uniform distribution of Li deposits. These improvements are attributed to the increased stability of the solid electrolyte interphase, which is confirmed by using a combination of electrochemical impedance spectroscopy, scanning electron microscopy, and X‐ray photoelectron spectroscopy. In contrast, it is found that the interphase in uncoated Li electrodes is likely affected by continuous reactions with the plasticizer, further confirming the need to use such protective coatings to achieve long‐term operation in practical solid‐state Li metal batteries.

Quality improvement initiatives in the acute care setting often target reduction of mortality and length of stay (LOS). Unplanned care escalations are associated with increased mortality risk and prolonged LOS, but may be precipitated by different factors, including appropriate triage, bed availability, and post-admission deterioration. This work evaluates different transfer timeframes to quantify the impact of deterioration-associated unplanned transfers to intensive care (ICU) on mortality and LOS, informing evidence-based interventions to improve patient care. This retrospective analysis examined 519 181 adult inpatients discharged from 15 hospitals in the United States. A propensity matched cohort analysis compared mortality and overall hospital LOS for patients admitted to routine and intermediate care units who did and did not have an unplanned ICU transfer within 12, 12-48, or ⩾48 hours from admission. Population cohorts were matched on age, sex, admitting unit type, admission type, and admission acuity. Multivariable regression analysis was used to estimate the impact of unplanned transfer on mortality and LOS. Sensitivity sub-analyses compared direct ICU admissions to unplanned ICU transfers using the same transfer timeframes and endpoints. Patients with unplanned transfers in each of three timeframes had statistically higher mortality rates and longer LOS than matched cohorts without unplanned transfer. Differences between cohorts was greatest in patients transferring ⩾48 hours post-admission for both mortality (25.1% vs 1.9%, P < .0001) and LOS (x¯ = 14.7 vs 5.3, P < .0001). Multivariate analysis showed unplanned ICU transfer significantly increased odds of mortality and prolonged LOS, with later transfers having the most profound influence (19-fold increase in mortality and 2-fold increase in LOS). Sensitivity analyses found a statistically significant increase in mortality and LOS associated with unplanned ICU transfer across all three timeframes. The association of later transfers with elevated mortality and LOS underscores the importance of timely intervention on patient deterioration.