Clinical Vignettes Research Articles

Fluid deresuscitation in critically ill children: comparing perspectives of intensivists and nephrologists

IntroductionFluid accumulation, presently defined as a pathologic state of overhydration/volume overload associated with clinical impact, is common and associated with worse outcomes. At times, deresuscitation, the active removal of fluid via diuretics or ultrafiltration, is necessary. There is no consensus regarding deresuscitation in children admitted to the pediatric intensive care unit. Little is known regarding perceptions and practices among pediatric intensivists and nephrologists regarding fluid provision and deresuscitation.MethodsCross-sectional electronic survey of pediatric nephrologists and intensivists from academic societies in the United States designed to better understand fluid management between disciplines. A clinical vignette was used to characterize the perceptions of optimal timing and method of deresuscitation initiation at four timepoints that correspond to different stages of shock.ResultsIn total, 179 respondents (140 intensivists, 39 nephrologists) completed the survey. Most 75.4% (135/179) providers believe discussing fluid balance and initiating fluid deresuscitation in pediatric intensive care unit (PICU) patients is “very important”. The first clinical vignette time point (corresponding to resuscitation phase of early shock) had the most dissimilarity between intensivists and nephrologists (p = 0.01) with regards to initiation of deresuscitation. However, providers demonstrated increasing agreement in their responses to initiate deresuscitation as the clinical vignette progressed. Compared to intensivists, nephrologists were more likely to choose “dialysis or ultrafiltration” as a deresuscitation method during the optimization [10.3 vs. 2.9% (p = 0.07)], stabilization [18.0% vs. 3.6% (p < 0.01)], and evacuation [48.7% vs. 23.6% (p < 0.01)] phases of shock. Conversely, intensivists were more likely to utilize scheduled diuretics than nephrologists [47.1% vs. 28.2% (p = 0.04)] later on in the patient course.DiscussionMost physicians believe that discussing fluid balance and deresuscitation is important. Nevertheless, when to initiate deresuscitation and how to accomplish it differed between nephrologist and intensivists. Widely understood and operationalizable definitions, further research, and eventually evidence-based guidelines are needed to help guide care.

Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial.

Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves physician diagnostic reasoning. To assess the effect of an LLM on physicians' diagnostic reasoning compared with conventional resources. A single-blind randomized clinical trial was conducted from November 29 to December 29, 2023. Using remote video conferencing and in-person participation across multiple academic medical institutions, physicians with training in family medicine, internal medicine, or emergency medicine were recruited. Participants were randomized to either access the LLM in addition to conventional diagnostic resources or conventional resources only, stratified by career stage. Participants were allocated 60 minutes to review up to 6 clinical vignettes. The primary outcome was performance on a standardized rubric of diagnostic performance based on differential diagnosis accuracy, appropriateness of supporting and opposing factors, and next diagnostic evaluation steps, validated and graded via blinded expert consensus. Secondary outcomes included time spent per case (in seconds) and final diagnosis accuracy. All analyses followed the intention-to-treat principle. A secondary exploratory analysis evaluated the standalone performance of the LLM by comparing the primary outcomes between the LLM alone group and the conventional resource group. Fifty physicians (26 attendings, 24 residents; median years in practice, 3 [IQR, 2-8]) participated virtually as well as at 1 in-person site. The median diagnostic reasoning score per case was 76% (IQR, 66%-87%) for the LLM group and 74% (IQR, 63%-84%) for the conventional resources-only group, with an adjusted difference of 2 percentage points (95% CI, -4 to 8 percentage points; P = .60). The median time spent per case for the LLM group was 519 (IQR, 371-668) seconds, compared with 565 (IQR, 456-788) seconds for the conventional resources group, with a time difference of -82 (95% CI, -195 to 31; P = .20) seconds. The LLM alone scored 16 percentage points (95% CI, 2-30 percentage points; P = .03) higher than the conventional resources group. In this trial, the availability of an LLM to physicians as a diagnostic aid did not significantly improve clinical reasoning compared with conventional resources. The LLM alone demonstrated higher performance than both physician groups, indicating the need for technology and workforce development to realize the potential of physician-artificial intelligence collaboration in clinical practice. ClinicalTrials.gov Identifier: NCT06157944.

Assessing the need for coronary angiography in high-risk acute coronary syndrome patients using artificial intelligence and computed tomography

Abstract Background/Introduction The use of coronary computed tomography angiography (CCTA) in managing high-risk acute coronary syndrome (ACS) patients is increasingly common. Yet, its role remains unvalidated in this context, posing challenges to traditional clinical decision-making algorithms. Physicians, accustomed to a hierarchical and structured approach involving symptom assessment, electrocardiograms, and biomarkers for deciding on the necessity of invasive coronary angiography (ICA), now face dilemmas when CCTA results challenge established diagnostic pathways and contradict clinical decisions based on usual criteria. Meanwhile, the potential of artificial intelligence (AI) which processes information in a way fundamentally different from human clinical reasoning, has been shown to aid decision-making in cardiovascular medicine. Yet, it has not been tested in this acute setting. Purpose This study aimed to evaluate the efficacy of the Chat Generative Pre-trained Transformer (ChatGPT) in guiding the need for ICA in high-risk ACS patients based on both standard clinical data and CCTA findings. Methods This investigation is a sub-study of a larger research project where high-risk ACS patients (all having a theoretical guideline-based indication for ICA) underwent CCTA prior to ICA. ChatGPT analyzed clinical vignettes containing patient data, electrocardiograms, troponin levels, and CCTA results to determine the necessity of ICA. The AI's recommendations were then compared to actual ICA findings to assess its decision-making accuracy. Results Among 86 patients with a clear indication for ICA, ChatGPT recommended against it for 19 patients, 16 of whom indeed had no significant findings at ICA (Figure). For 67 patients, ChatGPT advised proceeding with ICA, and a significant lesion was confirmed in 58 of them. Consequently, ChatGPT's overall accuracy stood at 86%, with a sensitivity of 95% and a specificity of 64%. The model's negative predictive value was 84%, and its positive predictive value was 87%. Conclusion These preliminary data suggest that ChatGPT can effectively assist in guiding the need for ICA among high-risk ACS patients, potentially reducing unnecessary procedures but further clinical studies to refine AI's role in clinical decision-making are required.ChatGPT's decision as per ICA's results

ClinicAl verSus algorIthmic predictioN of Obstructive Coronary Artery Disease - the CASINO study

Abstract Background The estimation of pretest probability (PTP) is a key step when evaluating patients with suspected coronary artery disease (CAD). Current European guidelines recommend using an algorithm based on age, sex, and symptom typicality. However, many cardiologists do not regularly use this method, relying instead on overall clinical impression. The aim of this study was to compare clinical and algorithmic prediction of obstructive CAD in a set of symptomatic patients with suspected chronic coronary syndrome (CCS). Methods In this survey study, 10 cardiologists from were asked to estimate the probability of obstructive CAD (on a scale of 1-99% and without the aid of any score or algorithm) for 120 anonymized clinical vignettes of outpatients who underwent diagnostic workup for suspected CCS. The provided information included age, sex, symptom description, cardiovascular risk factors, and know comorbidities. Whenever non-invasive tests had been performed, physicians were also asked to estimate the probability of obstructive CAD after knowing that results. Obstructive CAD was defined as diameter stenosis ≥50% confirmed by invasive coronary angiography. Results Among the 120 included patients (62 women, mean age 61±10 years), symptoms consisted of chest pain in 104 and dyspnea in the remainder. The median PTP of obstructive CAD was 15% (IQR 10-25%) according to the ESC algorithm, and 30% (IQR 15-50%) according to cardiologists’ estimates (p<0.001). The observed prevalence of obstructive CAD was 20% (n=24), yielding suitable calibration for the ESC model (predicted vs. observed p=0.631), and significant overestimation for clinicians’ assessments (predicted vs. observed p=0.014) – Figure 1A. Despite using the same data, cardiologists’ PTP estimates varied widely within the same patients (mean inter-observer coefficient of variation 56%±22%). In a subset of 29 patients whose PTP was appraised twice by the same physicians, the mean absolute difference between assessments (intra-observer variability) was 22%±8%. The ESC algorithm showed fair discriminative power to identify patients with obstructive CAD (C-statistic 0.68, 95% CI 0.58-0.79, p=0.005), whereas the C-statistic for clinicians ranged from 0.53 to 0.72 (average 0.62±0.05, p values for comparisons 0.001-0.677) – Fig 1B. In the subset of 70 patients with prior non-invasive tests, clinicians significantly changed their predictions after knowing test results (mean absolute difference 22%±12%, p<0.001), improving their discriminative power significantly (mean change in C-statistic of 0.08±0.09, p=0.020). Conclusion Clinicians’ assessments of PTP in symptomatic patients display high inter- and intra-observer variability, and tend to overestimate the likelihood of obstructive CAD. Despite using more clinical data, cardiologists fail to outperform the ESC algorithm. The systematic use of this tool should be promoted in order to improve disease prediction and guide non-invasive testing.

Healthcare providers’ psychological investment in clinical recommendations: Investigating the role of implicit racial attitudes

Racial disparities in clinical recommendations can result in racial disparities in health. While healthcare providers' implicit racial attitudes (affective component of bias) are theorized to be one major factor contributing to racial disparities in clinical recommendations, empirical evidence to support the link is lacking. This study aimed to bridge this gap by moving beyond the standard approach of operationalizing the quality of clinical recommendations as a guideline-consistent vs. -inconsistent dichotomy. The present secondary study examined the role of provider implicit racial attitudes in the quality of clinical recommendations, operationalized as behaviors reflecting providers’ psychological investment in patient care (i.e., number of words used to describe clinical recommendations, and number of treatment options recommended). Two-hundred-and-ten White medical trainees reviewed a clinical vignette of either a White or Black male patient and provided clinical recommendations. Their implicit racial attitudes were evaluated using the Implicit Association Test. Participants with more biased implicit racial attitudes (i.e., stronger implicit preference for White vs. Black individuals) used fewer words to describe their clinical recommendations and provided fewer clinical recommendations for the Black (vs. White) patient, while there were no significant differences between Black and White patients among participants with less biased implicit racial attitudes. These results illustrate the insidious impact of implicit racial attitudes in healthcare provision and underscore the need for researchers to consider the complex, nuanced ways in which provider implicit racial attitudes might manifest in clinical decision-making.

When end of treatment situations challenge patient-centered care: a discussion paper proposing new theoretical insights

IntroductionAccording to the Institute of Medicine patient-centered medicine is one of the six crucial dimensions of health care quality. Although the patient-centered care model is widely recognized for its ethical underpinnings and effectiveness, its practical implementation still raises challenges, especially in end-of-treatment situations. This discussion paper offers an overview of the challenges facing the physician-patient relationship in end-of-treatment situations.MethodsWe developed three clinical vignettes and made some theoretical considerations about ethical issues related to the decision-making process leading to the end of treatment.ResultsWe identified two main challenges that end-of-treatment situations pose to patient-centered care: (1) when the patient’s autonomy challenges the best clinical treatment; and (2) when the proposed treatment (discontinuation of treatment) challenges the patient’s preferences.DiscussionsPatient-centered care supports personalized decision-making, in which the physician’s approach varies according to the patient’s situation and individuality. The idea of beneficence may change during care, because of acceptance of the patient’s principles or a change in the primary goal of care.

P10.06.A AGENT-BASED MODELING FOR PATIENT-CENTERED CLINICAL DECISION SUPPORT IN NEURO-ONCOLOGY

Abstract BACKGROUND Complex clinical decision-making in neuro-oncology is a multifaceted process involving numerous specialties influenced by various objective factors and patient preferences. Modeling these multidisciplinary neuro-oncology discussions presents significant challenges, particularly given the multimodal nature of the data. Recent advancements in large language models (LLMs) have enabled the development of LLM “agents”. These agents pave the way for multi-agent systems capable of simulating interacting components, thus capturing the nuances of interdisciplinary clinical discussions. MATERIAL AND METHODS Utilizing GPT-4, we developed conversational agents with an agent-based modeling (ABM) approach; each agent was assigned a specific specialty. We simulated their interactions in various clinical decision-making scenarios. Lastly, we simulated 30 patient-agents and included them in the ABM system. We applied this ABM approach to a dataset of 16 clinical vignettes extracted from the UCSF Clinical Data Warehouse of primary adult glioma patients who underwent tumor board (TB) reviews. The TB decision was used as ground truth for quantitative evaluation. Seven independent clinicians evaluated the final decisions. RESULTS Our quantitative assessment showed that, when blinded to whether ABM or TB generated responses, evaluators preferred ABM recommendations 47.8% of the time, followed by TB recommendations (38%). The highest recommendation concordance was between TB and ABM (50.6%). The ABM was deemed clinically accurate in 76.8% of cases and provided clinically appropriate reasoning in 73.2%. Our qualitative assessment showed that including the patient agent significantly influenced the course of discussions, notably when the patient agents participated in the system by stating their healthcare goals. The most recommended individual treatment was standard-of-care chemotherapy, followed by targeted therapies, clinical trials, immunotherapy, and palliative care. The least frequently recommended management options were radiation, surgery, consultation with other specialties, and devices. CONCLUSION This study underscores the potential of ABM in neuro-oncology, particularly in simulating complex, real-world scenarios. ABM not only empowers patients but also offers clinicians a novel tool for understanding and navigating the multifaceted landscape of patient preferences and treatment options. It provides a platform for clinicians to visualize the potential impacts of various decisions, enhancing their ability to provide personalized, effective care. We envision future work where ABM systems are patient-centric, enabling patients to explore the potential impacts of their individual characteristics on clinical management options.

Disputed Boundaries of the Self, the Group, and their Environment: What We Learn from Refugees about our Psychic Functioning1.

One of Jung's most significant contributions concerns the mysterious, inexplicable and always out-of-reach nature of the self. In this paper, I will focus on the borders of the self and their nature, location and dynamics of maintenance and change in geographically, historically, and culturally situated subjects. Reflecting on the refugee experience, I intendto gain more insights into our psychic functioning and the dynamics of the self in relation to itself, the other and groups. The experiences of some refugees, marked bysignificant trauma and migration, shed light on how the boundaries of the self are frequently contested and perpetually negotiated with others, and how our subjectivity is shaped by ongoing dynamics of occupation, dispute and/or negotiation, conducted at various levels of our social and individual existence. My argument is that these processes occur at a specific site:the boundaries of the self, involving intrapsychic, interpersonal and group psychological dynamics, with reverberations in the socio-political and cultural spheres, and reciprocal influences between all these levels. This paper aims to concentrate on the shifts in these boundaries, illustrated through clinical vignettes.

An exploratory study on the potential of ChatGPT as an AI-assisted diagnostic tool for visceral leishmaniasis

Visceral leishmaniasis (VL) is a severe parasitic disease that poses significant diagnostic challenges due to its complex presentation and the necessity for comprehensive diagnostic methods. This exploratory study investigates the potential of Chat Generative Pre-trained Transformer (ChatGPT)/GPT-4, an artificial intelligence (AI) chatbot, in assisting the diagnostic process for VL. We evaluated the diagnostic accuracy of ChatGPT/GPT-4 in generating differential diagnosis lists for eight clinical vignette cases of VL, authored by a Brazilian infectious disease doctor. Our findings reveal that ChatGPT/GPT-4 included VL in the top five differential diagnoses in 75% of the cases (95% confidence interval [CI]: 40.1 – 93.7%) and identified VL as the top diagnosis in 50% of the cases (95% CI: 30.3 – 86.5%). These results underscore the high potential of ChatGPT/GPT-4 as an AI-assisted diagnostic tool, which is capable of providing accurate differential diagnoses and assisting healthcare professionals in resource-limited settings. The study highlights the broader applicability of AI chatbots in medical diagnostics, not only for common conditions but also for specialized and less prevalent diseases like VL. By integrating AI tools into the diagnostic workflow, healthcare providers can enhance their diagnostic accuracy and efficiency, ultimately improving patient outcomes. This research contributes to the growing body of evidence supporting the utility of AI in healthcare and underscores the need for further studies to validate these findings across larger and more diverse clinical scenarios.

Bioethics at the bedside: considering the adolescent voice in withdrawal of life-sustaining therapy.

Durable mechanical circulatory devices are commonly used to support children and adolescents in end-stage heart failure. However, these patients remain at high risk of acute medical complications, which may lead to significant impairment in functional capacity, altered quality of life, or death. We explore the incorporation of adolescent directives into medical decision-making in this scenario through a clinical case vignette.

Italian survey about intraperitoneal drain use in distal pancreatectomy.

Intraperitoneal prophylactic drain (IPD) use in distal pancreatectomy (DP) is still controversial. A survey was carried out through the Italian community of pancreatic surgeons using institutional emails, Twitter, and Facebook accounts of the Italian Association for the Study of the Pancreas (AISP) and the Italian Association of Hepato-biliary-pancreatic Surgery (AICEP). The survey was structured to learn surgeons' practice in using IPD through questions and one clinical vignette. Respondents were asked to report their regrets for omission and commission regarding the IPD use for the clinical scenario, eliciting a scale between 0 (no regret) and 100 (maximum regret). The threshold model and a multilevel mixed regression were built to identify respondents' attitudes. One hundred six surgeons completed the survey. Sixty-three (59.4%) respondents confirmed using at least 1 drain, while 43 (40.6%) placed 2 IPDs. Only 13 respondents (12.3%) declared a change in IPD strategy in patients at high risk of clinically relevant postoperative pancreatic fistula (CR-POPF), while 9 (9.4%) respondents changed their strategy in low-risk POPF situations. Thirty-five (35.5%) respondents declared they would remove the IPD within the third postoperative day (POD) in the absence of CR-POPF suspicion. The median omission regret, which proved to be the wrong decision, was 80 (50-100, IQR). The median regret due to the commission of IPD, which turned out to be useless, was 2.5 (1-20, IQR). The CR-POPF probability threshold at which drainage omission was the less regrettable choice was 7% (1-35, IQR). The threshold to perceive drain omission as the least regrettable choice was higher in female surgeons (P < 0.001), in surgeons who modulated the strategies based on the risk of CR-POPF, and in high volume centers (p = 0.039). The threshold was lower in surgeons who performed minimally invasive distal pancreatectomy (P < 0.001), adopted a closed system (P < 0.001), placed two IPDs (P < 0.001), or perceived the IPD as important to prevent reintervention (p = 0.047). Drain management after DP remains very heterogeneous among surgeons. The regret model suggested that IPD omission could be performed in low-risk patients (7% of CR-POPF), leading to low regret in the case of the wrong decision, making it an acceptable clinical decision.

Changes in Outcomes Expectations During the Cochlear Implant Evaluation Process.

Patient expectations are a critical factor in determining cochlear implant (CI) candidacy. However, minimal data are available on how potential CI recipients develop their expectations and if expectations can be modified by providers. In addition, there is little insight into the resources patients use to inform their decision to undergo implantation. This project aims to assess (1) the role of the CI evaluation (CIE) process on patients' expectations, (2) the extent to which patients' pre-CI outcome expectations can be modified, (3) the information patients use to inform their expectations, and patients' preferences for the discussion/display of potential CI outcomes. Prospective mixed methods study of 32 adult CI patients undergoing CIEs. Outcome measures included: pre-CI Cochlear Implant Quality of Life-35 Profile scores (CIQOL-35 Profile); pre-CIE/post-CIE/day of surgery CIQOL-Expectations scores; post-CIE/day of surgery Decisional Conflict Scale (DCS) scores; and pre-CI aided CNC-word and AzBio sentence scores. Thematic analyses of key informant interviews with 19 potential CI recipients were also performed. In aim 1, CI CIQOL-Expectation domain scores remained essentially unchanged following the CIE when averaged across all participants (d = 0.01 to 0.17). However, changes in expectations were observed for many participants at the individual level. Regarding the second aim, participants with higher pre-CIE expectations showed a decrease in expectations following the CIE for all CIQOL domains except emotional and social (d = -0.27 to -0.77). In contrast, the only significant change in participants with lower expectations was an increase in expectations in the environment score from pre-CIE to the day of surgery (d = 0.76). Expectations remained essentially unchanged or continued to change in the same direction between the post-CIE and the day of surgery, narrowing the gap between participants with higher and lower expectations. Overall, participants demonstrated low overall conflict related to their decision to proceed with cochlear implantation (mean DCS of 11.4 post-CIE and 14.2 at time of surgery out of 100) but DCS scores were higher for participants with lower pre-CIE expectations (d = 0.71). In aim 3, key informant interviews demonstrated no differences between the low- and high expectation cohorts regarding resources used to develop their perception of CI outcomes. Potential CI recipients placed high value in talking with patients who had previously received a CI, and preferred discussing CI functional abilities via clinical vignettes described in the CIQOL Functional Staging System rather than by discussing speech recognition or CIQOL-35 Profile scores. The results of the present study suggest that, although overall expectations averaged across the cohort remained essentially unchanged, individual participants' pre-CI expectations can be modified and there is value in measuring these expectations using the CIQOL-Expectations tool to determine if they are realistic. This information can then be utilized during personalized counseling to present a more accurate representation of likely CI outcomes for each patient. Discussions between potential CI recipients and current CI users may also provide valuable information to inform their expectations. In addition, communicating potential CI benefits using CIQOL functional stages and associated clinical vignettes may result in more realistic patient expectations and support shared decision-making related to CI surgery.

Doubting love: identification and stigma towards relationship obsessive-compulsive disorder

ABSTRACT Objective Obsessive-compulsive disorder (OCD) is a clinically heterogeneous condition with a variety of symptom dimensions. The aim of this study was to assess the ability of university students and the lay public to identify a lesser-known theme of obsessive-compulsive symptoms known as relationship obsessive-compulsive disorder (ROCD) and to examine factors that impact identification and stigma of this symptom subtype. Method A total of 258 participants (M = 24.1 years, SD = 7.61) completed an online survey. A series of clinical vignettes depicted hypothetical characters with ROCD, varied by symptom presentation (partner-focused, relationship-centred) and gender (female, male), as well as a contamination OCD vignette for comparison purposes. Results As expected, participants showed significantly higher misidentification rates for ROCD (88.3%) compared to the contamination vignettes (28.7%). ROCD was most often misidentified as relationship issues and worry. Although partner-focused ROCD was not misidentified more frequently than relationship-centred ROCD, partner-focused vignettes were significantly more stigmatised than relationship-centred vignettes as predicted (with the male, partner-focused vignette most stigmatised). Conclusions High levels of misidentification of ROCD may contribute to the existing lag between OCD symptom onset and proper treatment, emphasising the need for greater education and media focus on the heterogeneity of OCD.

Enhancing diagnostic accuracy in symptom-based health checkers: a comprehensive machine learning approach with clinical vignettes and benchmarking.

The development of machine learning models for symptom-based health checkers is a rapidly evolving area with significant implications for healthcare. Accurate and efficient diagnostic tools can enhance patient outcomes and optimize healthcare resources. This study focuses on evaluating and optimizing machine learning models using a dataset of 10 diseases and 9,572 samples. The dataset was divided into training and testing sets to facilitate model training and evaluation. The following models were selected and optimized: Decision Tree, Random Forest, Naive Bayes, Logistic Regression and K-Nearest Neighbors. Evaluation metrics included accuracy, F1 scores, and 10-fold cross-validation. ROC-AUC and precision-recall curves were also utilized to assess model performance, particularly in scenarios with imbalanced datasets. Clinical vignettes were employed to gauge the real-world applicability of the models. The performance of the models was evaluated using accuracy, F1 scores, and 10-fold cross-validation. The use of ROC-AUC curves revealed that model performance improved with increasing complexity. Precision-recall curves were particularly useful in evaluating model sensitivity in imbalanced dataset scenarios. Clinical vignettes demonstrated the robustness of the models in providing accurate diagnoses. The study underscores the importance of comprehensive model evaluation techniques. The use of clinical vignette testing and analysis of ROC-AUC and precision-recall curves are crucial in ensuring the reliability and sensitivity of symptom-based health checkers. These techniques provide a more nuanced understanding of model performance and highlight areas for further improvement. This study highlights the significance of employing diverse evaluation metrics and methods to ensure the robustness and accuracy of machine learning models in symptom-based health checkers. The integration of clinical vignettes and the analysis of ROC-AUC and precision-recall curves are essential steps in developing reliable and sensitive diagnostic tools.

The Contactful Quad: An Effective Tool for Working With Trauma

The purpose of this paper is to explore the potential and development of the drama triangle, the winner’s triangle, and the trauma quadrangle in trauma work and to offer a tool to help identify the roles/patterns replayed when caught in drama. This fosters moving into a more active (winner’s) space and retaining a more integrated position. The authors draw on trauma-informed practices and transactional analysis (TA). They propose that the bystander position in response to trauma can be both an internal and external process. As a tool for working with developmental trauma, the authors offer the contactful quad, which through empowerment and witness aims to support the process of decontamination and deconfusion, to aid the healing process, and to support integration of the unresolved somatic experience of trauma responses. Using clinical vignettes, the authors demonstrate the power of the Actively Courageous position of the contactful quad as an antidote to the Bystander position.

Essentials of Genomics in Nursing Undergraduate Education: A Discussion Paper

AimTo map the 2021 American Association of Colleges of Nursing Essentials to the American Nurses Association Essentials of Genomic Nursing for all nurses and provide resources for nursing faculty to support the seamless integration of genomics into existing undergraduate curricula. BackgroundSince the completion of the Human Genome Project in 2003, rapid advancements in genomic science leading to practical applications of genomics have revolutionized all areas of healthcare. Nursing is built on foundational life sciences, including genomics. As the largest segment of the healthcare workforce, who spend the most time with patients and families, nurses play a critical role in healthcare teams integrating genomic knowledge into patient care to improve health and well-being. Consequently, nurses must be equipped with foundational genomic knowledge and skills during their undergraduate education. However, there is wide variability in whether and how nursing programs have incorporated genomics into their curricula. Additionally, nursing faculty may have limited knowledge of foundational genomic concepts and lack confidence in teaching genomics. DesignDiscussion paper MethodsWe aligned domains from the American Association of Colleges of Nursing Essentials and American Nurses Association Essentials of Genomic Nursing. ResultsA map illustrating alignment in multiple areas, which provide examples of ways to integrate genomics into existing nursing curricula. ConclusionAlthough based on domains developed in the United States, the map, curricular resources, example learning outcomes and clinical vignettes can be used by nursing faculty globally to prepare future nurses who are competent in providing genomics-informed nursing care on entry to practice.

Current management of hepatobiliary malignancies between centers with or without a liver transplant program: A multi-society national survey

BackgroundAvailability of liver transplantation (LT) as a treatment for hepatocellular carcinoma (HCC) and other liver malignancies may determine heterogeneity of therapeutic strategies across different centers. AimsTo investigate the practice between hepato-biliary centers without (HB centers) and with a LT program (LT centers), we launched a 38-item web-based national survey, with directors of centers as a target. MethodsThe survey, including 4 clinical vignettes, collected data on their approach to HCC and transplant oncology. ResultsAfter duplicates removal, 75 respondents were considered. Respondents from LT centers (n = 22, 29.3 %) were more in favor of LT in the case of HCC outside Milan criteria (90.9 % vs. 67.9 %, p = 0.037), recurrent HCC (95.5 % vs. 50.9 %, p = 0.002) and other malignancies such as cholangiocarcinoma or neuroendocrine tumors. No significant difference was observed concerning the proportion of centers favorable to LT for unresectable colorectal liver metastases (100 % vs. 88.7 %, p = 0.100). ConclusionThis national survey showed how management of HCC and awareness of transplant oncology may differ between HB and LT centers. Effective networking between HB and LT centers is crucial to provide optimal treatment and access to LT.

Analysis of ChatGPT in the Triage of Common Spinal Complaints

ChatGPT is a natural language processing chatbot with a significant prevalence in modern media with a clear application in the medical triage workflow. ChatGPT has shown significant capacity for understanding clinical vignettes, radiology reports, and even passing the American Board of Neurological Surgery board examination. There has never been an evaluation of the chatbot in triage and diagnosing spinal vignettes common to primary and urgent care practice. Fifteen clinical scenarios were created to mimic spinal complaints common to primary and urgent care scenarios. GPT-4 was instructed to assess the situation as if it was in a primary care office and determine diagnosis, imaging recommendations, and if emergency room (ER) or operative referral was necessary. Answers were recorded and the results compared to those of attending and resident respondents. GPT-4 provided the most likely diagnosis in each scenario. Additionally, it recommended reasonable clinical management of each scenario which would fall within standard practice guidelines. ChatGPT tended toward over-referral to the ER; however, this was not significant. GPT-4 was noninferior in all categories when compared to respondents. ChatGPT is a powerful tool for primary triage of spinal issues. It can rapidly and accurately evaluate clinical scenarios and provide clear diagnostic reasoning. GPT-4 is not designed for medical use and will provide a disclaimer as such. It did tend toward over-referring patients to the ER. With specific training, it is likely that artificial intelligence and natural language processing chatbots will become widely used in primary triage of spinal issues.

Psychoanalysis and homosexuality: The relevance of homophobia and shame for all sexualities

This article will describe some contemporary developments in psychoanalytic thinking about sexuality, from a post‐heteronormative standpoint. Psychoanalytic theorising of homosexuality had been generally homophobic for decades, reflecting society stereotypes. Previously, many analysts prescribed treatment to convert “the homosexual patient” to “healthy heterosexuality”, which Freud had regarded as likely to be unsuccessful. The author argues that psychoanalysis, having been part of the “disease” of homophobia, and the lengthy shadow cast by shame, can also contribute to its “cure” by promoting a broader understanding of the conscious and unconscious nature of desire and identity. The author considers the central role played by the superego in maintaining shame’s dominance and explores the impact of shame on time and temporality, including shame’s unique après coup nature. The struggle for acceptance of non‐heteronormative sexuality is discussed and contemporary developments in theorising desire, internalised homophobia, and Oedipus are explored. Drawing on fictional composite clinical vignettes, the author describes current challenges that clinicians face in relation to homophobia and shame, illustrating the central importance for clinicians to address where shame and homophobia lie within their own internal worlds and object relationships.

Can AI-Generated Clinical Vignettes in Japanese Be Used Medically and Linguistically?

Creating clinical vignettes requires considerable effort. Recent developments in generative artificial intelligence (AI) for natural language processing have been remarkable and may allow for the easy and immediate creation of diverse clinical vignettes. In this study, we evaluated the medical accuracy and grammatical correctness of AI-generated clinical vignettes in Japanese and verified their usefulness. Clinical vignettes were created using the generative AI model GPT-4-0613. The input prompts for the clinical vignettes specified the following seven elements: (1) age, (2) sex, (3) chief complaint and time course since onset, (4) physical findings, (5) examination results, (6) diagnosis, and (7) treatment course. The list of diseases integrated into the vignettes was based on 202 cases considered in the management of diseases and symptoms in Japan's Primary Care Physicians Training Program. The clinical vignettes were evaluated for medical and Japanese-language accuracy by three physicians using a five-point scale. A total score of 13 points or above was defined as "sufficiently beneficial and immediately usable with minor revisions," a score between 10 and 12 points was defined as "partly insufficient and in need of modifications," and a score of 9 points or below was defined as "insufficient." Regarding medical accuracy, of the 202 clinical vignettes, 118 scored 13 points or above, 78 scored between 10 and 12 points, and 6 scored 9 points or below. Regarding Japanese-language accuracy, 142 vignettes scored 13 points or above, 56 scored between 10 and 12 points, and 4 scored 9 points or below. Overall, 97% (196/202) of vignettes were available with some modifications. Overall, 97% of the clinical vignettes proved practically useful, based on confirmation and revision by Japanese medical physicians. Given the significant effort required by physicians to create vignettes without AI, using GPT is expected to greatly optimize this process.