BACKGROUND: With recent advancements in the treatment of chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL), healthcare specialists may face challenges making treatment and management decisions based on latest evidence for the optimal care of patients with these conditions. This study aimed to identify specific knowledge, skills, and confidence gaps impacting the treatment of CLL and MCL, to inform future educational activities.
METHODS: Hematologists and hemato-oncologists (HCPs, n = 224) from France (academic settings), Germany, and the United States (academic and community settings) responded to a 15-minute quantitative needs assessment survey that measured perceived knowledge, skills, and confidence levels regarding different aspects of treatment and management of CLL and MCL patients, as well as clinical case questions. Descriptive statistics (cross tabulations) and Chi-square tests were conducted.
RESULTS: Four areas of educational need were identified: (1) sub-optimal knowledge of treatment guidelines; (2) sub-optimal knowledge of molecular testing to inform CLL/MCL treatment decisions; (3) sub-optimal skills when making treatment decisions according to patient profile (co-morbidities, molecular testing results); and (4) challenges balancing the risk of toxicities with benefits of treatment. Over one-third of the respondents reported skill gaps when selecting suitable treatment options and prescribing therapies and reported a lack in confidence to initiate and manage treatment. Larger gaps in knowledge of guidelines and skills in patient assessment were identified in MCL, compared to CLL.
CONCLUSIONS: This study suggests the need for continuing medical education specifically to improve knowledge of treatment guidelines, and to assist clinicians in developing skills and confidence when faced with clinical decision-making scenarios of patients with specific comorbidities and/or molecular test results, for example, through case-based learning activities.

OBJECTIVE: Systematic reviews and medical guidelines are widely used in clinical practice. However, these are often not up-to-date and focussed on the average patient. We therefore aimed to evaluate a guideline add-on, TherapySelector (TS), which is based on monthly updated data of all available high-quality studies, classified in specific patient profiles.
METHODS: We evaluated the TS for the treatment of hepatitis C (HCV) in an international cohort of patients treated with direct-acting antivirals between 2015 and 2020. The primary outcome was the number of patients receiving one of the two preferred treatment options of the HCV TS, based on the highest level of evidence, cure rate, absence of ribavirin-associated adverse effects, and treatment duration.
RESULTS: We enrolled 567 patients. The number of patients treated with one of the two preferred treatment options according to the HCV TS ranged between 27% (2015) and 60% (2020; p < 0.001). Most of the patients received a regimen with a longer treatment-duration (up to 34%) and/or addition of ribavirin (up to 14%). The effect on the expected cure-rate was minimal (1-6% higher) when the first preferred TherapySelector option was given compared to the actual treatment.
CONCLUSIONS: Medical decision-making can be optimised by a guideline add-on; in HCV its use appears to minimise adverse effects and cost. The use of such an add-on might have a greater impact in diseases with suboptimal cure-rates, high costs or adverse effects, for which treatment options rely on specific patient characteristics.

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Artificial intelligence (AI) in the form of ChatGPT has rapidly attracted attention from physicians and medical educators. While it holds great promise for more routine medical tasks, may broaden one\'s differential diagnosis, and may be able to assist in the evaluation of images, such as radiographs and electrocardiograms, the technology is largely based on advanced algorithms akin to pattern recognition. One of the key questions raised in concert with these advances is: What does the growth of artificial intelligence mean for medical education, particularly the development of critical thinking and clinical reasoning? In this commentary, we will explore the elements of cognitive theory that underlie the ways in which physicians are taught to reason through a diagnostic case and compare hypothetico-deductive reasoning, often employing illness scripts, with inductive reasoning, which is based on a deeper understanding of mechanisms of health and disease. Issues of cognitive bias and their impact on diagnostic error will be examined. The constructs of routine and adaptive expertise will also be delineated. The application of artificial intelligence to diagnostic problem solving, along with concerns about racial and gender bias, will be delineated. Using several case examples, we will demonstrate the limitations of this technology and its potential pitfalls and outline the direction medical education may need to take in the years to come.

BACKGROUND: Guidelines must be interpreted comprehensively and correctly to standardize the clinical process. However, this process is challenging and requires interpreters to have a medical background and qualifications. In this study, the accuracy of ChatGPT3.5 in answering clinical questions related to the 2019 guidelines for severe acute pancreatitis was evaluated.
RESULTS: An observational study was conducted using the 2019 guidelines for severe acute pancreatitis. The study compared the accuracy of ChatGPT3.5 in English versus Chinese and found that it was more accurate in English (71%) than in Chinese (59%) (P value: 0.203). Additionally, the study assessed the accuracy of ChatGPT3.5 in answering short-answer questions versus true/false questions and found that it was more accurate in answering short-answer questions (76%) than in answering true/false questions (60%) (P value: 0.405).
CONCLUSIONS: For clinicians managing severe acute pancreatitis, ChatGPT3.5 may have potential value. However, it should not be relied upon excessively for clinical decision making.

BACKGROUND: Surrogates, proxies, and clinicians making shared treatment decisions for patients who have lost decision-making capacity often fail to honor patients\' wishes, due to stress, time pressures, misunderstanding patient values, and projecting personal biases. Advance directives intend to align care with patient values but are limited by low completion rates and application to only a subset of medical decisions. Here, we investigate the potential of large language models (LLMs) to incorporate patient values in supporting critical care clinical decision-making for incapacitated patients in a proof-of-concept study.
METHODS: We simulated text-based scenarios for 50 decisionally incapacitated patients for whom a medical condition required imminent clinical decisions regarding specific interventions. For each patient, we also simulated five unique value profiles captured using alternative formats: numeric ranking questionnaires, text-based questionnaires, and free-text narratives. We used pre-trained generative LLMs for two tasks: 1) text extraction of the treatments under consideration and 2) prompt-based question-answering to generate a recommendation in response to the scenario information, extracted treatment, and patient value profiles. Model outputs were compared with adjudications by three domain experts who independently evaluated each scenario and decision.
CONCLUSIONS: Automated extractions of the treatment in question were accurate for 88% (n = 44/50) of scenarios. LLM treatment recommendations received an average Likert score by the adjudicators of 3.92 of 5.00 (five being best) across all patients for being medically plausible and reasonable treatment recommendations, and 3.58 of 5.00 for reflecting the documented values of the patient. Scores were highest when patient values were captured as short, unstructured, and free-text narratives based on simulated patient profiles. This proof-of-concept study demonstrates the potential for LLMs to function as support tools for surrogates, proxies, and clinicians aiming to honor the wishes and values of decisionally incapacitated patients.

BACKGROUND: The Advisory Committee on Immunization Practices issued a shared clinical decision-making (SCDM) recommendation for HPV vaccination in persons aged 27-45. Since expanded eligibility for the vaccine was issued, little information has been available about HPV vaccine behaviors and intentions among women in this age group.
METHODS: We conducted a cross-sectional online survey among women aged 27-45 years recruited through a Qualtrics™ respondent panel (N = 324) to answer the following questions (1) What is the prevalence of HPV vaccination among a diverse sample of adult women aged 27-45 years? (2) What are the characteristics of those who have or have not previously been vaccinated? and (3) What factors are associated with the intention to obtain the HPV vaccine among those who had never been vaccinated? Multivariable logistic regression analyses estimated adjusted odds ratios (AORs) and 95% confidence intervals (95% CIs).
RESULTS: Only 31.1% had at least one dose of the HPV vaccine. In multivariable analyses, those more likely to have been vaccinated were younger and were more likely to believe that the vaccine was effective. Of those unvaccinated or unsure, 54.8% indicated they were likely to get vaccinated in the future. Factors associated with future vaccine intention (compared to those not intending) included beliefs about vaccine testing, perceived likelihood of HPV infection, greater comfort in asking one\'s provider for vaccination, and prior negative healthcare experiences.
CONCLUSIONS: Our findings suggest that many women in this age group are interested in HPV vaccination. While the recommendation is for SCDM rather than routine vaccination for all women in this age group, efforts to promote informed decision-making among mid-adult women may include educating women about the rigorous vaccine testing and approval process, their risk factors for HPV infection, and encouraging them to engage in SCDM with their medical providers. Targeted efforts to reach women who have had negative experiences with healthcare may also be needed.

Cone-beam computed tomography (CBCT) has proven to be a safe and effective adjunctive imaging tool for interventional radiology. Nevertheless, limited studies have examined the application of CBCT in renal artery embolization (RAE). The objective of this study is to evaluate the role of CBCT in intra-procedural decision-making for RAE. This multicenter retrospective study included 40 consecutive patients (age: 55.9 ± 16.5 years; male, 55%) who underwent CBCT during RAE from January 2019 to January 2023. The additional information provided by CBCT was classified into Category 1 (no additional information), Category 2 (more information without changing the treatment plan), and Category 3 (valuable information that led to a change in the treatment plan). CBCT did not add unique information for four patients (10%) classified as Category 1. CBCT clarified ambiguous angiographic findings and confirmed the existing treatment plan for 19 patients (47.5%) graded as Category 2; complex vascular anatomy was explained (n = 13), and a correlation between vascular territory and target lesion was established (n = 6). CBCT offered valuable information that was not visible on digital subtraction angiography and changed the treatment plan for 17 patients categorized as Category 3; a mismatch between the vascular territory and the target lesion led to the identification of alternative (n = 3) and additional feeders (n = 8); and the extent of embolization was reduced by using automatic feeder detection software (n = 6). CBCT is an efficient tool that aids in the decision-making process during the embolization procedure by providing supplementary imaging information. This additional information enables the confident identification of target vessels, facilitates superselective embolization, prevents non-target embolization, and helps locate missing feeders.

UNASSIGNED: This study aimed to explore the views and perceptions of Advanced Life Support (ALS) practitioners in two South African provinces on initiating, withholding, and terminating resuscitation in OHCA.
UNASSIGNED: Semi-structured one-on-one interviews were conducted with operational ALS practitioners working within the prehospital setting in the Western Cape and Free State provinces. Recorded interviews were transcribed and subjected to inductive-dominant, manifest content analysis. After familiarisation with the data, meaning units were condensed, codes were applied and collated into categories that were then assessed, reviewed, and refined repeatedly.
UNASSIGNED: A total of 18 ALS providers were interviewed. Five main categories were developed from the data analysis: 1) assessment of prognosis, 2) internal factors affecting decision-making, 3) external factors affecting decision-making, 4) system challenges, and 5) ideas for improvement. Factors influencing the assessment of prognosis were history, clinical presentation, and response to resuscitation. Internal factors affecting decision-making were driven by emotion and contemplation. External factors affecting decision-making included family, safety, and disposition. System challenges relating to bystander response and resources were identified. Ideas for improvement in training and support were brought forward.
UNASSIGNED: Many factors influence OHCA decision-making in the Western Cape and Free State provinces, and numerous system challenges have been identified. The findings of this study can be used as a frame of reference for prehospital emergency care personnel and contribute to the development of context-specific guidelines.