OBJECTIVE: Cerebrovascular events during thrombolysis in cardiac arrest (CA) caused by pulmonary embolism (PE) is a life-threatening condition. However, the balance between cerebrovascular events and thrombolytic therapy in PE-induced CA remains a great challenge.
METHODS: In this study, we reported three unique cases regarding main concerns surrounding cerebrovascular events in thrombolytic therapy in PE-induced CA.
RESULTS: The patient in the case 1 treated with thrombolysis during CPR and finally discharged neurologically intact. The patient in the case 2 received delayed thrombolysis and died eventually. The patient in the case 3 was contraindicated to thrombolysis due to the complication of subarachioid hemorrahage and died within days.
CONCLUSIONS: Our case series highlights three proposed approaches to consider before administering thrombolysis as a treatment option in PE-induced CA patients: (1) prolonging the resuscitation, (2) administering thrombolysis promptly, and (3) ruling out cerebrovascular events.

OBJECTIVE: This study aimed to compare the impact of case-based learning (CBL) versus lecture-based learning (LBL) on dental students\' clinical decision-making regarding DF severity using Visual Analog Scale (VAS) scoring.
METHODS: Eighty first-year graduate dental students were randomly assigned to either the CBL (n = 38) or LBL (n = 42) groups. Both groups received instruction on DF diagnosis, with CBL involving small group sessions analyzing real cases and LBL involving traditional lectures. Effectiveness was assessed by presenting 32 dental fluorosis cases with Thylstrup-Fejerskov Index (TSIF) scores ranging from 0 to 7 through slide presentations to both groups for VAS assessment. Five evaluators of each group randomly selected were asked to repeat the rating 2 weeks later. Statistical analysis included two-way ANOVA for group and gender differences, intra-class correlation coefficient (ICC) for reliability, and Spearman correlation coefficients for validity.
RESULTS: Variations in VAS scores were observed between CBL and LBL groups, with no significant gender impact. Excellent inter- and intra-evaluator agreement was found for VAS scoring in both groups, indicating its reliability. Validation against established indices (such as DI and TSIF) demonstrated strong correlations, with CBL students exhibiting higher correlations.
CONCLUSIONS: CBL enhances students\' clinical decision-making and proficiency in DF diagnosis, as evidenced by more consistent and accurate VAS scoring compared to LBL. These findings highlight the importance of innovative educational strategies in dental curricula, with implications for improving training quality and clinical outcomes.
BACKGROUND: The study was registered at the Clinical Research Center, Hospital of Stomatology, Wuhan University (Registration code: HGGC-036).

UNASSIGNED: Medical curricula implicitly teach that race has a biological basis. Clinical rotations reinforce this misconception as race-based algorithms are used to guide clinical decision-making. This module aims to expose the fallacy of race in clinical algorithms, using the estimated glomerular filtration rate (eGFR) equation as an example.
UNASSIGNED: We created a 60-minute module in consultation with nephrologists. The format was an interactive, case-based presentation with a didactic section. A third-year medical student facilitated the workshops to medical students. Evaluation included pre/post surveys using 5-point Likert scales to assess awareness regarding use of race as a biological construct. Higher scores indicated increased awareness.
UNASSIGNED: Fifty-five students participated in the module. Pre/post results indicated that students significantly improved in self-perceived knowledge of the history of racism in medicine (2.6 vs. 3.2, p < .001), awareness of race in clinical algorithms (2.7 vs. 3.7, p < .001), impact of race-based eGFR on quality of life/treatment outcomes (4.5 vs. 4.8, p = .01), differences between race and ancestry (3.7 vs. 4.3, p < .001), and implications of not removing race from the eGFR equation (2.7 vs. 4.2, p < .001). Students rated the workshops highly for quality and clarity.
UNASSIGNED: Our module expands on others\' work to expose the fallacy of race-based algorithms and define its impact on health equity. Limitations include a lack of objective assessment of knowledge acquisition. We recommend integrating this module into preclinical and clinical curricula to discuss the use of race in medical literature and clinical practice.

OBJECTIVE: To develop scores for predicting the need for artificial airway procedures for acute trauma patients in the emergency department (ED).
METHODS: Retrospective case-control.
METHODS: A tertiary comprehensive hospital in China.
METHODS: 8288 trauma patients admitted to the ED within 24 hours of injury and who were admitted from 1 August 2012 to 31 July 2020.
METHODS: The study outcome was the establishment of an artificial airway within 24 hours of admission to the ED. Based on the different feature compositions, two scores were developed in the development cohort by multivariable logistic regression. The predictive performance was assessed in the validation cohort.
RESULTS: The O-SPACER (Oxygen saturation, Systolic blood pressure, Pulse rate, Age, Coma Scale, Eye response, Respiratory rate) score was developed based on the patient\'s basic information with an area under the curve (AUC) of 0.85 (95% CI 0.80 to 0.89) in the validation group. Based on the basic information and trauma scores, the IO-SPACER (Injury Severity Score, Oxygen saturation, Systolic blood pressure, Pulse rate, Age, Coma Scale, Eye response, Respiratory rate) score was developed, with an AUC of 0.88 (95% CI 0.84 to 0.92). According to the O-SPACER and IO-SPACER scores, the patients were stratified into low, medium and high-risk groups. According to these two scores, the high-risk patients were associated with an increased demand for artificial airways, with an OR of 40.16-40.67 compared with the low-risk patients.
CONCLUSIONS: The O-SPACER score provides risk stratification for injured patients requiring urgent airway intervention in the ED and may be useful in guiding initial management. The IO-SPACER score may assist in further determining whether the patient needs planned intubation or tracheotomy early after trauma.

OBJECTIVE: This prospective cohort study evaluated the effect of unfolding case-based learning on undergraduate nursing students\' self-perceived clinical decision-making ability.
METHODS: Students\' self-reported responses to Jenkins\'s Clinical Decision Making in Nursing Scale were compared between the unfolding case-based learning cohort (n=140) and the comparison cohort (n=126) at a school of nursing in the United States.
RESULTS: The results revealed similar students\' responses between the two study cohorts. However, unfolding case-based learning significantly increased students\' perceived proficiency in \"search for information and unbiased assimilation of new information\".
CONCLUSIONS: Findings from the present study highlight possibilities presented by unfolding case-based learning in undergraduate nursing education. The study supports that unfolding case studies can be introduced early on, and then nurtured throughout the undergraduate program to influence the development of nursing students\' clinical decision-making skills.

Adverse drug reactions are a common cause of morbidity in health care. The US Food and Drug Administration (FDA) evaluates individual case safety reports of adverse events (AEs) after submission to the FDA Adverse Event Reporting System as part of its surveillance activities. Over the past decade, the FDA has explored the application of artificial intelligence (AI) to evaluate these reports to improve the efficiency and scientific rigor of the process. However, a gap remains between AI algorithm development and deployment. This viewpoint aims to describe the lessons learned from our experience and research needed to address both general issues in case-based reasoning using AI and specific needs for individual case safety report assessment. Beginning with the recognition that the trustworthiness of the AI algorithm is the main determinant of its acceptance by human experts, we apply the Diffusion of Innovations theory to help explain why certain algorithms for evaluating AEs at the FDA were accepted by safety reviewers and others were not. This analysis reveals that the process by which clinicians decide from case reports whether a drug is likely to cause an AE is not well defined beyond general principles. This makes the development of high performing, transparent, and explainable AI algorithms challenging, leading to a lack of trust by the safety reviewers. Even accounting for the introduction of large language models, the pharmacovigilance community needs an improved understanding of causal inference and of the cognitive framework for determining the causal relationship between a drug and an AE. We describe specific future research directions that underpin facilitating implementation and trust in AI for drug safety applications, including improved methods for measuring and controlling of algorithmic uncertainty, computational reproducibility, and clear articulation of a cognitive framework for causal inference in case-based reasoning.

Artificial intelligence use is increasing exponentially, including by patients in medical decision- making. Because of the limitations of chatbots and the possibility of receiving erroneous or incomplete information, patient.

OBJECTIVE: Real-world evidence (RWE)-derived from analysis of real-world data (RWD)-has the potential to guide personalized treatment decisions. However, because of potential confounding, generating valid RWE is challenging. This study demonstrates how to responsibly generate RWE for treatment decisions. We validate our approach by demonstrating that we can uncover an existing adjuvant chemotherapy (ACT) guideline for stage II and III colon cancer (CC)-which came about using both data from randomized controlled trials and expert consensus-solely using RWD.
METHODS: Data from the population-based Netherlands Cancer Registry from a total of 27,056 patients with stage II and III CC who underwent curative surgery were analyzed to estimate the overall survival (OS) benefit of ACT. Focusing on 5-year OS, the benefit of ACT was estimated for each patient using G-computation methods by adjusting for patient and tumor characteristics and estimated propensity score. Subsequently, on the basis of these estimates, an ACT decision tree was constructed.
RESULTS: The constructed decision tree corresponds to the current Dutch guideline: patients with stage III or stage II with T stage 4 should receive surgery and ACT, whereas patients with stage II with T stage 3 should only receive surgery. Interestingly, we do not find sufficient RWE to conclude against ACT for stage II with T stage 4 and microsatellite instability-high (MSI-H), a recent addition to the current guideline.
CONCLUSIONS: RWE, if used carefully, can provide a valuable addition to our construction of evidence on clinical decision making and therefore ultimately affect treatment guidelines. Next to validating the ACT decisions advised in the current Dutch guideline, this paper suggests additional attention should be paid to MSI-H in future iterations of the guideline.

BACKGROUND: Large language models (LLMs) are computational artificial intelligence systems with advanced natural language processing capabilities that have recently been popularized among health care students and educators due to their ability to provide real-time access to a vast amount of medical knowledge. The adoption of LLM technology into medical education and training has varied, and little empirical evidence exists to support its use in clinical teaching environments.
OBJECTIVE: The aim of the study is to identify and qualitatively evaluate potential use cases and limitations of LLM technology for real-time ward-based educational contexts.
METHODS: A brief, single-site exploratory evaluation of the publicly available ChatGPT-3.5 (OpenAI) was conducted by implementing the tool into the daily attending rounds of a general internal medicine inpatient service at a large urban academic medical center. ChatGPT was integrated into rounds via both structured and organic use, using the web-based \"chatbot\" style interface to interact with the LLM through conversational free-text and discrete queries. A qualitative approach using phenomenological inquiry was used to identify key insights related to the use of ChatGPT through analysis of ChatGPT conversation logs and associated shorthand notes from the clinical sessions.
RESULTS: Identified use cases for ChatGPT integration included addressing medical knowledge gaps through discrete medical knowledge inquiries, building differential diagnoses and engaging dual-process thinking, challenging medical axioms, using cognitive aids to support acute care decision-making, and improving complex care management by facilitating conversations with subspecialties. Potential additional uses included engaging in difficult conversations with patients, exploring ethical challenges and general medical ethics teaching, personal continuing medical education resources, developing ward-based teaching tools, supporting and automating clinical documentation, and supporting productivity and task management. LLM biases, misinformation, ethics, and health equity were identified as areas of concern and potential limitations to clinical and training use. A code of conduct on ethical and appropriate use was also developed to guide team usage on the wards.
CONCLUSIONS: Overall, ChatGPT offers a novel tool to enhance ward-based learning through rapid information querying, second-order content exploration, and engaged team discussion regarding generated responses. More research is needed to fully understand contexts for educational use, particularly regarding the risks and limitations of the tool in clinical settings and its impacts on trainee development.

This case challenges the conventional preference for coronary artery bypass grafting (CABG) over percutaneous coronary intervention (PCI) in patients with diabetes, left main coronary artery disease (LMCAD) and multivessel disease. Current guidelines generally recommend CABG, especially in the context of LMCAD. However, our case involves a male patient with diabetes with LMCAD and extensive multivessel disease who was successfully treated with PCI, demonstrating a favorable outcome. Despite the high-risk profile, including a SYNTAX score of 28, the PCI approach was selected. This decision was supported by evidence suggesting comparable outcomes between PCI and CABG in similar patients. Our case highlights the potential of PCI as not just a viable, but potentially superior alternative in specific high-risk patients with diabetes, contrary to the prevailing belief in favor of CABG for all patients with left main involvement.