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In the field of health care, knowledge and clinical reasoning are key with regard to quality and confidence in decision making. The development of knowledge and clinical reasoning is influenced not only by students' intrinsic factors but also by extrinsic factors such as satisfaction with taught content, pedagogic resources and pedagogic methods, and the nature of the objectives and challenges proposed. Nowadays, professors play the role of learning facilitators rather than simple \"lecturers\" and face students as active learners who are capable of attributing individual meanings to their personal goals, challenges, and experiences to build their own knowledge over time. Innovations in health simulation technologies have led to clinical virtual simulation. Clinical virtual simulation is the recreation of reality depicted on a computer screen and involves real people operating simulated systems. It is a type of simulation that places people in a central role through their exercising of motor control skills, decision skills, and communication skills using virtual patients in a variety of clinical settings. Clinical virtual simulation can provide a pedagogical strategy and can act as a facilitator of knowledge retention, clinical reasoning, improved satisfaction with learning, and finally, improved self-efficacy. However, little is known about its effectiveness with regard to satisfaction, self-efficacy, knowledge retention, and clinical reasoning. This study aimed to evaluate the effect of clinical virtual simulation with regard to knowledge retention, clinical reasoning, self-efficacy, and satisfaction with the learning experience among nursing students. A randomized controlled trial with a pretest and 2 posttests was carried out with Portuguese nursing students (N=42). The participants, split into 2 groups, had a lesson with the same objectives and timing. The experimental group (n=21) used a case-based learning approach, with clinical virtual simulator as a resource, whereas the control group (n=21) used the same case-based learning approach, with recourse to a low-fidelity simulator and a realistic environment. The classes were conducted by the usual course lecturers. We assessed knowledge and clinical reasoning before the intervention, after the intervention, and 2 months later, with a true or false and multiple-choice knowledge test. The students' levels of learning satisfaction and self-efficacy were assessed with a Likert scale after the intervention. The experimental group made more significant improvements in knowledge after the intervention (P=.001; d=1.13) and 2 months later (P=.02; d=0.75), and it also showed higher levels of learning satisfaction (P<.001; d=1.33). We did not find statistical differences in self-efficacy perceptions (P=.9; d=0.054). The introduction of clinical virtual simulation in nursing education has the potential to improve knowledge retention and clinical reasoning in an initial stage and over time, and it increases the satisfaction with the learning experience among nursing students.

Objective To quantify the accuracy and reproducibility of pathologists’ diagnoses of melanocytic skin lesions.Design Observer accuracy and reproducibility study.Setting 10 US states.Participants Skin biopsy cases (n=240), grouped into sets of 36 or 48. Pathologists from 10 US states were randomized to independently interpret the same set on two occasions (phases 1 and 2), at least eight months apart.Main outcome measures Pathologists’ interpretations were condensed into five classes: I (eg, nevus or mild atypia); II (eg, moderate atypia); III (eg, severe atypia or melanoma in situ); IV (eg, pathologic stage T1a (pT1a) early invasive melanoma); and V (eg, ≥pT1b invasive melanoma). Reproducibility was assessed by intraobserver and interobserver concordance rates, and accuracy by concordance with three reference diagnoses.Results In phase 1, 187 pathologists completed 8976 independent case interpretations resulting in an average of 10 (SD 4) different diagnostic terms applied to each case. Among pathologists interpreting the same cases in both phases, when pathologists diagnosed a case as class I or class V during phase 1, they gave the same diagnosis in phase 2 for the majority of cases (class I 76.7%; class V 82.6%). However, the intraobserver reproducibility was lower for cases interpreted as class II (35.2%), class III (59.5%), and class IV (63.2%). Average interobserver concordance rates were lower, but with similar trends. Accuracy using a consensus diagnosis of experienced pathologists as reference varied by class: I, 92% (95% confidence interval 90% to 94%); II, 25% (22% to 28%); III, 40% (37% to 44%); IV, 43% (39% to 46%); and V, 72% (69% to 75%). It is estimated that at a population level, 82.8% (81.0% to 84.5%) of melanocytic skin biopsy diagnoses would have their diagnosis verified if reviewed by a consensus reference panel of experienced pathologists, with 8.0% (6.2% to 9.9%) of cases overinterpreted by the initial pathologist and 9.2% (8.8% to 9.6%) underinterpreted.Conclusion Diagnoses spanning moderately dysplastic nevi to early stage invasive melanoma were neither reproducible nor accurate in this large study of pathologists in the USA. Efforts to improve clinical practice should include using a standardized classification system, acknowledging uncertainty in pathology reports, and developing tools such as molecular markers to support pathologists’ visual assessments.

\"Have you ever looked at a heavy volume on neuropsychology and wondered what it would actually be like to become a professional clinician, working every day with neurological patients in a busy hospital while simultaneously learning your craft? This book tells the story of that journey. a The Notebook of a New Clinical Neuropsychologist vividly details the experience of starting work in clinical neuropsychology, exploring early-career learning and development through an intimate, case-based approach. Topics include the learning of basic clinical skills and knowledge, counter-transference, the clinician's emotional experiences, ethical and moral dilemmas, and the development of clinical reasoning. The book is structured around individual studies from the author's early caseload, with each vignette containing the relevant neuropathology, clinical presentation, history, neuropsychological test finding and other clinical data. Chapters are also organized around key neuropathological conditions, including traumatic brain injury, stroke, and brain infections, which provide a broader context for the narrative focus of the book. A few academic books explore the personal, intellectual and ethical dilemmas that face a new clinician working with patients in a neuropsychological setting. Tailored to facilitate experiential learning via case studies, reflective practice and problem based-learning, the book will be of interest to students and professionals working within the broad area of neuropsychology and brain injury services.\"--Publisher description.

This study aimed to develop two scales to measure nursing students' clinical emotional intelligence (Clin-EI) and clinical competency (Clin-COM) and evaluate the effects of hybrid simulation on their clinical EI and competency. Hybrid clinical simulation training prepares learners for a complex and demanding clinical environment, facilitates practice readiness and develops a sense of emotional stability. Low emotional intelligence (EI) can compromise patient safety and quality of care. This study applied experimental research design and a randomized controlled trial was conducted. Two hundred and twelve nursing students were randomly selected, dividing them into group A (exposed to traditional clinical training) and group B (exposed to hybrid simulation along with traditional clinical training). Exploratory Factor Analysis results revealed a three-factor model for the Clin-EI tool and a one-factor model for Clin-COM. Internal consistency indicators by factor level of Clin-EI (⍺ = 0.86–0.95) and Clin-COM (⍺ = 0.98) indicate good to excellent. The acquired clinical EI, clinical competency and OSCE performance of group B were higher compared with group A with mean differences of −0.889, −0.887 and −7.08 respectively, p-values were all < .001. The factors effect sizes appeared negatively Clin-EI (-0.830), Clin-COM (-0.757) and OSCE (-0.606). All variables have strong significant correlations (p-values <0.001) within both groups. The combination of traditional clinical teaching and hybrid simulation has positively influenced the acquired EI, clinical competency and OSCE performance of nursing students. Developing a high level of clinical EI and competency ensures safe nursing practice. ●Hybrid simulation is a powerful clinical training strategy to improve EI and competency, ensuring practice readiness.●A combination of traditional clinical teaching and hybrid simulation positively influenced EI and clinical competency.●A high level of EI through hybrid simulation training facilitates clinical competency, ensuring safe nursing practice.

Objective structured clinical examinations (OSCEs) are a widely recognized and accepted method to assess clinical competencies but are often resource-intensive. This study aimed to evaluate the feasibility and effectiveness of a virtual reality (VR)-based station (VRS) compared with a traditional physical station (PHS) in an already established curricular OSCE. Fifth-year medical students participated in an OSCE consisting of 10 stations. One of the stations, emergency medicine, was offered in 2 modalities: VRS and PHS. Students were randomly assigned to 1 of the 2 modalities. We used 2 distinct scenarios to prevent content leakage among participants. Student performance and item characteristics were analyzed, comparing the VRS with PHS as well as with 5 other case-based stations. Student perceptions of the VRS were collected through a quantitative and qualitative postexamination online survey, which included a 5-point Likert scale ranging from 1 (minimum) to 5 (maximum), to evaluate the acceptance and usability of the VR system. Organizational and technical feasibility as well as cost-effectiveness were also evaluated. Following randomization and exclusions of invalid data sets, 57 and 66 participants were assessed for the VRS and PHS, respectively. The feasibility evaluation demonstrated smooth implementation of both VR scenarios (septic and anaphylactic shock) with 93% (53/57) of students using the VR technology without issues. The difficulty levels of the VRS scenarios (septic shock: P=.67; anaphylactic shock: P=.58) were comparable to the average difficulty of all stations (P=.68) and fell within the reference range (0.4-0.8). In contrast, VRS demonstrated above-average values for item discrimination (septic shock: r'=0.40; anaphylactic shock: r'=0.33; overall r'=0.30; with values >0.3 considered good) and discrimination index (septic shock: D=0.25; anaphylactic shock: D=0.26; overall D=0.16, with 0.2-0.3 considered mediocre and <0.2 considered poor). Apart from some hesitancy toward its broader application in future practical assessments (mean 3.07, SD 1.37 for VRS vs mean 3.65, SD 1.18 for PHS; P=.03), there were no other differences in perceptions between VRS and PHS. Thematic analysis highlighted the realistic portrayal of medical emergencies and fair assessment conditions provided by the VRS. Regarding cost-effectiveness, initial development of the VRS can be offset by long-term savings in recurring expenses like standardized patients and consumables. Integration of the VRS into the current OSCE framework proved feasible both technically and organizationally, even within the strict constraints of short examination phases and schedules. The VRS was accepted and positively received by students across various levels of technological proficiency, including those with no prior VR experience. Notably, the VRS demonstrated comparable or even superior item characteristics, particularly in terms of discrimination power. Although challenges remain, such as technical reliability and some acceptance concerns, VR remains promising in applications of clinical competence assessment.

Deep learning has the potential to augment clinician performance in medical imaging interpretation and reduce time to diagnosis through automated segmentation. Few studies to date have explored this topic. To develop and apply a neural network segmentation model (the HeadXNet model) capable of generating precise voxel-by-voxel predictions of intracranial aneurysms on head computed tomographic angiography (CTA) imaging to augment clinicians' intracranial aneurysm diagnostic performance. In this diagnostic study, a 3-dimensional convolutional neural network architecture was developed using a training set of 611 head CTA examinations to generate aneurysm segmentations. Segmentation outputs from this support model on a test set of 115 examinations were provided to clinicians. Between August 13, 2018, and October 4, 2018, 8 clinicians diagnosed the presence of aneurysm on the test set, both with and without model augmentation, in a crossover design using randomized order and a 14-day washout period. Head and neck examinations performed between January 3, 2003, and May 31, 2017, at a single academic medical center were used to train, validate, and test the model. Examinations positive for aneurysm had at least 1 clinically significant, nonruptured intracranial aneurysm. Examinations with hemorrhage, ruptured aneurysm, posttraumatic or infectious pseudoaneurysm, arteriovenous malformation, surgical clips, coils, catheters, or other surgical hardware were excluded. All other CTA examinations were considered controls. Sensitivity, specificity, accuracy, time, and interrater agreement were measured. Metrics for clinician performance with and without model augmentation were compared. The data set contained 818 examinations from 662 unique patients with 328 CTA examinations (40.1%) containing at least 1 intracranial aneurysm and 490 examinations (59.9%) without intracranial aneurysms. The 8 clinicians reading the test set ranged in experience from 2 to 12 years. Augmenting clinicians with artificial intelligence-produced segmentation predictions resulted in clinicians achieving statistically significant improvements in sensitivity, accuracy, and interrater agreement when compared with no augmentation. The clinicians' mean sensitivity increased by 0.059 (95% CI, 0.028-0.091; adjusted P = .01), mean accuracy increased by 0.038 (95% CI, 0.014-0.062; adjusted P = .02), and mean interrater agreement (Fleiss κ) increased by 0.060, from 0.799 to 0.859 (adjusted P = .05). There was no statistically significant change in mean specificity (0.016; 95% CI, -0.010 to 0.041; adjusted P = .16) and time to diagnosis (5.71 seconds; 95% CI, 7.22-18.63 seconds; adjusted P = .19). The deep learning model developed successfully detected clinically significant intracranial aneurysms on CTA. This suggests that integration of an artificial intelligence-assisted diagnostic model may augment clinician performance with dependable and accurate predictions and thereby optimize patient care.