BACKGROUND: Bleeding following cardiac surgery is common and serious, yet a gap persists in understanding how experienced intensive care nurses identify and respond to such complications.
OBJECTIVE: To describe the clinical decision-making of experienced intensive care unit nurses in addressing bleeding after cardiac surgery.
METHODS: This qualitative study adopted the Recognition-Primed Decision Model as its theoretical framework. Thirty-nine experienced nurses from four adult intensive care units participated in semi-structured interviews based on the critical decision method. The interviews explored their clinical judgements and decisions in bleeding situations, and data were analysed through dimensional analysis, an alternative to grounded theory.
RESULTS: Participants maintained consistent vigilance towards post-cardiac surgery bleeding, recognizing it through a haemorrhagic dimension associated with blood loss and chest drainage and a hypovolemic dimension focusing on the repercussions of reduced blood volume. These dimensions organized their understanding of bleeding types (i.e., normal, medical, surgical, tamponade) and necessary actions. Their decision-making encompassed monitoring bleeding, identifying the cause, stopping the bleeding, stabilizing haemodynamic and supporting the patient and family. Participants also adapted their actions to specific circumstances, including local practices, professional autonomy, interprofessional dynamics and resource availability.
CONCLUSIONS: Nurses\' decision-making was shaped by their personal attributes, the patient\'s condition and contextual circumstances, underscoring their expertise and pivotal role in anticipating actions and adapting to diverse conditions. The concept of actionability emerged as the central dimension explaining their decision-making, defined as the capability to implement actions towards specific goals within the possibilities and constraints of a situation.
CONCLUSIONS: This study underscores the need for continual updates to care protocols to align with current evidence and for quality improvement initiatives to close existing practice gaps. Exploring the concept of actionability further, developing adaptability-focused educational programmes, and understanding decision-making intricacies are crucial for informing nursing education and decision-support systems.

Virtual patients (VPs) have long been used to teach and assess clinical reasoning. VPs can be programmed to simulate authentic patient-clinician interactions and to reflect a variety of contextual permutations. However, their use has historically been limited by the high cost and logistical challenges of large-scale implementation. We describe a novel globally-accessible approach to develop low-cost VPs at scale using artificial intelligence (AI) large language models (LLMs). We leveraged OpenAI Generative Pretrained Transformer (GPT) to create and implement two interactive VPs, and created permutations that differed in contextual features. We used systematic prompt engineering to refine a prompt instructing ChatGPT to emulate the patient for a given case scenario, and then provide feedback on clinician performance. We implemented the prompts using GPT-3.5-turbo and GPT-4.0, and created a simple text-only interface using the OpenAI API. GPT-4.0 was far superior. We also conducted limited testing using another LLM (Anthropic Claude), with promising results. We provide the final prompt, case scenarios, and Python code. LLM-VPs represent a \'disruptive innovation\' - an innovation that is unmistakably inferior to existing products but substantially more accessible (due to low cost, global reach, or ease of implementation) and thereby able to reach a previously underserved market. LLM-VPs will lay the foundation for global democratization via low-cost-low-risk scalable development of educational and clinical simulations. These powerful tools could revolutionize the teaching, assessment, and research of management reasoning, shared decision-making, and AI evaluation (e.g. \'software as a medical device\' evaluations).

We present the case of a 17-year-old adolescent boy admitted to the Pediatric Intensive Care Unit with an extensive necrotizing soft tissue infection who subsequently developed altered mental status and autonomic instability. Altered mental status is a common occurrence in critically ill children with a broad differential of etiologies. After ruling out organic causes of encephalopathy, management is typically focused on avoiding deliriogenic agents, including benzodiazepines. Dopamine antagonist medications may also be administered adjunctively to manage agitation or delirium that is refractory to other measures. We review the workup and differential diagnosis for altered mentation in critically ill children and discuss the current understanding of a rare etiology of altered mental status in the pediatric population.

The clinical assessment of patients with proximal muscle weakness represents a frequent yet intricate challenge. We present the case of a 60-year-old woman who experienced a progressive, symmetrical weakness in proximal limbs and bulbar muscles over 6 months. Notable clinical findings included bilateral ophthalmoparesis; widespread muscle atrophy; and pronounced weakness in craniobulbar, cervical, and proximal muscles, more severe than in distal ones. We elucidate a methodical diagnostic approach, focusing on constructing a comprehensive differential diagnosis and identifying potential causes of proximal muscle weakness. Special emphasis is placed on exploring the etiologies in cases presenting with both progressive muscle weakness and ophthalmoparesis. We further describe the role of muscle biopsy results and their integration with genetic testing outcomes.

The Script Concordance Tests (SCTs) are an examination modality introduced by decree in the French National Ranking Exam for medical students in 2024. Their objective is to evaluate clinical reasoning in situations of uncertainty. In practice, SCTs assess the impact of new information on the probability of a hypothesis formulated a priori based on an authentic clinical scenario. This approach resembles probabilistic (or Bayesian) reasoning. Due to the uncertainty associated with the explored clinical situation, SCTs do not compare the student\'s response to an expected one in a theoretical knowledge reference. Instead, the distribution of responses from a panel of experienced physicians is used to establish the question\'s scoring scale. Literature data suggest that physicians, even experienced ones, like most humans, often exhibit biased intuitive probabilistic reasoning. These biases raise questions about the relevance of using expert panel responses as scoring scales for SCTs.

UNASSIGNED: This scoping review aims to describe how Bobath concept is conceptualized, operationalized, and studied in adult neurorehabilitation.
UNASSIGNED: The Joanna Briggs Institute (JBI) and the Preferred Reporting Items for Scoping Reviews (PRISMA-ScR) guidelines were adopted. Non-scientific and research articles were searched in electronic databases PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, ScienceDirect, and Physiotherapy Evidence Database (PEDro), with the keywords \"Bobath\" or \"Neurodevelopmental Treatment\", published in English, Spanish, and Portuguese, between 2013 and 2023.
UNASSIGNED: Of the 78 publications identified, 31 articles addressed the conceptual underpinnings of Bobath concept (seven theoretical papers, seven Delphi/surveys/mixed methods studies, four qualitative studies, one scoping review, 10 letters to the editor, and two editorials), comprising five themes: (a) theoretical principles; (b) clinical principles; (c) clinical reasoning; (d) conceptualizing movement; and, current (e) evidence debate. The revised definition and the Model of Bobath Clinical Practice provide a clarification of the unique aspects of Bobath concept. A new clinical skill was identified beyond facilitation - visuospatial kinesthetic perception - as well as how Bobath experts conceptualize movement, which are all integral to clinical reasoning.
UNASSIGNED: This review provides an updated Bobath clinical framework that gathers the theoretical foundations and clinical practice principles that require careful consideration in the design of future intervention studies.Implications for rehabilitationThis scoping review consolidates the clinical and theoretical principles of contemporary Bobath practice, providing a clear framework for clinicians.The Model of Bobath Clinical Practice (MBCP) framework enables detailed documentation of movement analysis and movement diagnosis, guiding clinical reasoning and interventions.This review identifies fundamental principles and practices to inform future Bobath intervention studies, ensuring their clinical relevance.A framework with specific recommendations has been developed to guide Bobath intervention studies, enhancing the integration of clinical practice, education, and research.

Clinical reasoning is considered one of the most important competencies but is not included in most healthcare curricula. The number and diversity of patient encounters are the decisive factors in the development of clinical reasoning competence. Physical real patient encounters are considered optimal, but virtual patient cases also promote clinical reasoning. A high-volume, low-fidelity virtual patient library thus can support clinical reasoning training in a safe environment and can be tailored to the needs of learners from different health care professions. It may also stimulate interprofessional understanding and team shared decisions. Implementation will be challenged by tradition, the lack of educator competence and prior experience as well as the high-density curricula at medical and veterinary schools and will need explicit address from curriculum managers and education leads.

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OBJECTIVE: To investigate approaches of reasoning with large language models (LLMs) and to propose a new prompting approach, ensemble reasoning, to improve medical question answering performance with refined reasoning and reduced inconsistency.
METHODS: We used multiple choice questions from the USMLE Sample Exam question files on 2 closed-source commercial and 1 open-source clinical LLM to evaluate our proposed approach ensemble reasoning.
RESULTS: On GPT-3.5 turbo and Med42-70B, our proposed ensemble reasoning approach outperformed zero-shot chain-of-thought with self-consistency on Steps 1, 2, and 3 questions (+3.44%, +4.00%, and +2.54%) and (2.3%, 5.00%, and 4.15%), respectively. With GPT-4 turbo, there were mixed results with ensemble reasoning again outperforming zero-shot chain-of-thought with self-consistency on Step 1 questions (+1.15%). In all cases, the results demonstrated improved consistency of responses with our approach. A qualitative analysis of the reasoning from the model demonstrated that the ensemble reasoning approach produces correct and helpful reasoning.
CONCLUSIONS: The proposed iterative ensemble reasoning has the potential to improve the performance of LLMs in medical question answering tasks, particularly with the less powerful LLMs like GPT-3.5 turbo and Med42-70B, which may suggest that this is a promising approach for LLMs with lower capabilities. Additionally, the findings show that our approach helps to refine the reasoning generated by the LLM and thereby improve consistency even with the more powerful GPT-4 turbo. We also identify the potential and need for human-artificial intelligence teaming to improve the reasoning beyond the limits of the model.

OBJECTIVE: Clinical decision-making (CDM) is crucial in pharmacy practice, necessitating effective teaching in undergraduate and postgraduate pharmacy education. This study aims to explore undergraduates and postgraduates\' perceptions of how a new teaching model supports their CDM when addressing patient cases.
METHODS: Implemented in a full-day CDM course for pharmacy students and a half-day course for pharmacists in the Netherlands, the model, accompanied by a learning guide, facilitated CDM in patient cases. Eight courses were conducted between September 2022 to June 2023, followed by an online survey measuring participants\' agreement on how the model supported their CDM, using a 5-point Likert scale. Additionally, three open-ended questions were included to elicit learning outcomes and self-development opportunities.
RESULTS: Of 175 invited participants, 159 (91%) completed the survey. Most agreed the teaching model supported their CDM, particularly in considering the patient\'s healthcare needs and context (96%), and exploring all available options (96%). Participants found the model provided a clear structure (97%), and fostered critical thinking (93%). The most frequently mentioned learning outcomes and self-development opportunities included collecting sufficient relevant information, maintaining a broad perspective, and decelerating the process to avoid premature closure.
CONCLUSIONS: Participants agreed that the teaching model helped them to make clinical decisions. Both undergraduate and postgraduate pharmacy education could possibly benefit from the teaching model\'s implementation in supporting pharmacy students and pharmacists conducting CDM in pharmacy practice.