柳田 育孝

BACKGROUND: 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. OBJECTIVE: In this study, we evaluated the medical accuracy and grammatical correctness of AI-generated clinical vignettes in Japanese and verified their usefulness. METHODS: 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." RESULTS: 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. CONCLUSION: 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.

BACKGROUND: An illness script is a specific script format geared to represent patient-oriented clinical knowledge organized around enabling conditions, faults (i.e., pathophysiological process), and consequences. Generative artificial intelligence (AI) stands out as an educational aid in continuing medical education. The effortless creation of a typical illness script by generative AI could help the comprehension of key features of diseases and increase diagnostic accuracy. No systematic summary of specific examples of illness scripts has been reported since illness scripts are unique to each physician. OBJECTIVE: This study investigated whether generative AI can generate illness scripts. METHODS: We utilized ChatGPT-4, a generative AI, to create illness scripts for 184 diseases based on the diseases and conditions integral to the National Model Core Curriculum in Japan for undergraduate medical education (2022 revised edition) and primary care specialist training in Japan. Three physicians applied a three-tier grading scale: "A" denotes that the content of each disease's illness script proves sufficient for training medical students, "B" denotes that it is partially lacking but acceptable, and "C" denotes that it is deficient in multiple respects. RESULTS: By leveraging ChatGPT-4, we successfully generated each component of the illness script for 184 diseases without any omission. The illness scripts received "A," "B," and "C" ratings of 56.0% (103/184), 28.3% (52/184), and 15.8% (29/184), respectively. CONCLUSION: Useful illness scripts were seamlessly and instantaneously created using ChatGPT-4 by employing prompts appropriate for medical students. The technology-driven illness script is a valuable tool for introducing medical students to key features of diseases.

BACKGROUND: Medical history contributes approximately 80% to a diagnosis, although physical examinations and laboratory investigations increase a physician's confidence in the medical diagnosis. The concept of artificial intelligence (AI) was first proposed more than 70 years ago. Recently, its role in various fields of medicine has grown remarkably. However, no studies have evaluated the importance of patient history in AI-assisted medical diagnosis. OBJECTIVE: This study explored the contribution of patient history to AI-assisted medical diagnoses and assessed the accuracy of ChatGPT in reaching a clinical diagnosis based on the medical history provided. METHODS: Using clinical vignettes of 30 cases identified in The BMJ, we evaluated the accuracy of diagnoses generated by ChatGPT. We compared the diagnoses made by ChatGPT based solely on medical history with the correct diagnoses. We also compared the diagnoses made by ChatGPT after incorporating additional physical examination findings and laboratory data alongside history with the correct diagnoses. RESULTS: ChatGPT accurately diagnosed 76.6% (23/30) of the cases with only the medical history, consistent with previous research targeting physicians. We also found that this rate was 93.3% (28/30) when additional information was included. CONCLUSIONS: Although adding additional information improves diagnostic accuracy, patient history remains a significant factor in AI-assisted medical diagnosis. Thus, when using AI in medical diagnosis, it is crucial to include pertinent and correct patient histories for an accurate diagnosis. Our findings emphasize the continued significance of patient history in clinical diagnoses in this age and highlight the need for its integration into AI-assisted medical diagnosis systems.

UNLABELLED: Kikuchi-Fujimoto disease (KFD), also called histiocytic necrotizing lymphadenitis, is more common in young women and typically presents with small, painful, localized cervical lymphadenopathy that resolves spontaneously within a few weeks. Laboratory findings are variable. As many as 40% of KFD cases are reported to be painless, and up to 22% to be generalized lymphadenopathy. Therefore, malignant lymphoma could be a differential diagnosis of KFD. A histopathologic diagnosis is needed when it is difficult to distinguish KFD from lymphoma. KFD typically shows small, highly accumulated cervical lymph nodes on fluorodeoxyglucose positron emission tomography (FDG-PET). This contrasts with malignant lymphoma, which tends to be associated with massive lymphadenopathy. In our case, a 40-year-old Japanese male presented with painless lumps in the right neck, accompanied by fever, night sweats, and loss of appetite. His symptoms and laboratory results worsened over a month. FDG-PET revealed highly accumulated uptake in cervical, mediastinal, and axillary lymph nodes. The PET imaging showed a small, high FDG uptake and contributed to the correct diagnosis of KFD. This case report highlights the importance of FDG-PET, which is a valuable diagnostic tool for KFD as it typically differentiates large clusters of small lymph nodes typical of KFD from normal lymph nodes. LEARNING POINTS: Kikuchi-Fujimoto disease (KFD) typically presents with small, painful, localised cervical lymphadenopathy.KFD has atypical patterns showing painless and generalised lymphadenopathy.Fluorodeoxyglucose positron emission tomography (FDG-PET) could be useful for diagnosing not only malignant lymphoma but also KFD.

Abstract Background Illness scripts, which are structured summaries of clinical knowledge concerning diseases, are crucial in disease prediction and problem representation during clinical reasoning. Clinicians iteratively enhance their illness scripts through their clinical practice. Because illness scripts are unique to each physician, no systematic summary of specific examples of illness scripts has been reported. Objective Generative artificial intelligence (AI) stands out as an educational aid in continuing medical education. The effortless creation of a typical illness script by generative AI could enhance the comprehension of disease concepts and increase diagnostic accuracy. This study investigated whether generative AI possesses the capability to generate illness scripts. Methods We used ChatGPT, a generative AI, to create illness scripts for 184 diseases based on the diseases and conditions integral to the National Model Core Curriculum for undergraduate medical education (2022 revised edition) and primary care specialist training in Japan. Three physicians applied a three-tier grading scale: “A” if the content of each disease’s illness script proves sufficient for training medical students, “B” if it is partially lacking but acceptable, and “C” if it is deficient in multiple respects. Moreover, any identified deficiencies in the illness scripts were discussed during the evaluation process. Results Leveraging ChatGPT, we successfully generated each component of the illness script for 184 diseases without any omission. The illness scripts received “A,” “B,” and “C” ratings of 56.0% (103/184), 28.3% (52/184), and 15.8% (29/184), respectively. Conclusion Useful illness scripts were seamlessly and instantaneously created by ChatGPT using prompts appropriate for medical students. The technology-driven illness script is a valuable tool for introducing medical students to disease conceptualization.

BACKGROUND: ChatGPT (OpenAI) has gained considerable attention because of its natural and intuitive responses. ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers, as stated by OpenAI as a limitation. However, considering that ChatGPT is an interactive AI that has been trained to reduce the output of unethical sentences, the reliability of the training data is high and the usefulness of the output content is promising. Fortunately, in March 2023, a new version of ChatGPT, GPT-4, was released, which, according to internal evaluations, was expected to increase the likelihood of producing factual responses by 40% compared with its predecessor, GPT-3.5. The usefulness of this version of ChatGPT in English is widely appreciated. It is also increasingly being evaluated as a system for obtaining medical information in languages other than English. Although it does not reach a passing score on the national medical examination in Chinese, its accuracy is expected to gradually improve. Evaluation of ChatGPT with Japanese input is limited, although there have been reports on the accuracy of ChatGPT's answers to clinical questions regarding the Japanese Society of Hypertension guidelines and on the performance of the National Nursing Examination. OBJECTIVE: The objective of this study is to evaluate whether ChatGPT can provide accurate diagnoses and medical knowledge for Japanese input. METHODS: Questions from the National Medical Licensing Examination (NMLE) in Japan, administered by the Japanese Ministry of Health, Labour and Welfare in 2022, were used. All 400 questions were included. Exclusion criteria were figures and tables that ChatGPT could not recognize; only text questions were extracted. We instructed GPT-3.5 and GPT-4 to input the Japanese questions as they were and to output the correct answers for each question. The output of ChatGPT was verified by 2 general practice physicians. In case of discrepancies, they were checked by another physician to make a final decision. The overall performance was evaluated by calculating the percentage of correct answers output by GPT-3.5 and GPT-4. RESULTS: Of the 400 questions, 292 were analyzed. Questions containing charts, which are not supported by ChatGPT, were excluded. The correct response rate for GPT-4 was 81.5% (237/292), which was significantly higher than the rate for GPT-3.5, 42.8% (125/292). Moreover, GPT-4 surpassed the passing standard (>72%) for the NMLE, indicating its potential as a diagnostic and therapeutic decision aid for physicians. CONCLUSIONS: GPT-4 reached the passing standard for the NMLE in Japan, entered in Japanese, although it is limited to written questions. As the accelerated progress in the past few months has shown, the performance of the AI will improve as the large language model continues to learn more, and it may well become a decision support system for medical professionals by providing more accurate information.

BACKGROUND<p>Medical history contributes approximately 80% to the diagnosis, although physical examinations and laboratory investigations increase a physician’s confidence in the medical diagnosis. The concept of artificial intelligence (AI] was first proposed more than 70 years ago. Recently, its role in various fields of medicine has grown remarkably. However, no studies have evaluated the importance of patient history in AI-assisted medical diagnosis.</p> OBJECTIVE<p>This study explored the contribution of patient history to AI-assisted medical diagnoses.</p> METHODS<p>Using 30 cases from clinical vignettes from the British Medical Journal, we evaluated the accuracy of diagnoses generated by the AI model ChatGPT. We compared the diagnoses made by ChatGPT based solely on the medical history with the correct diagnoses. We also compared the diagnoses made by ChatGPT after incorporating additional physical examination findings and laboratory data alongside the history with correct diagnoses.</p> RESULTS<p>ChatGPT accurately diagnosed 76.6% of the cases with the medical history alone, consistent with previous research targeting physicians. We also found that this rate was 93.3% when additional information was included.</p> CONCLUSIONS<p>Although adding additional information improves diagnostic accuracy, patient history remains a significant factor in AI-assisted medical diagnosis. Thus, when utilizing AI in medical diagnosis, it is crucial to include pertinent and correct patient histories for an accurate diagnosis. Our findings emphasize the continued significance of patient history in clinical diagnoses in this age and highlight the need for its integration into AI-assisted medical diagnosis systems.</p>

BACKGROUND: A clinical diagnostic support system (CDSS) can support medical students and physicians in providing evidence-based care. In this study, we investigate diagnostic accuracy based on the history of present illness between groups of medical students using a CDSS, Google, and neither (control). Further, the degree of diagnostic accuracy of medical students using a CDSS is compared with that of residents using neither a CDSS nor Google. METHODS: This study is a randomized educational trial. The participants comprised 64 medical students and 13 residents who rotated in the Department of General Medicine at Chiba University Hospital from May to December 2020. The medical students were randomly divided into the CDSS group (n = 22), Google group (n = 22), and control group (n = 20). Participants were asked to provide the three most likely diagnoses for 20 cases, mainly a history of a present illness (10 common and 10 emergent diseases). Each correct diagnosis was awarded 1 point (maximum 20 points). The mean scores of the three medical student groups were compared using a one-way analysis of variance. Furthermore, the mean scores of the CDSS, Google, and residents' (without CDSS or Google) groups were compared. RESULTS: The mean scores of the CDSS (12.0 ± 1.3) and Google (11.9 ± 1.1) groups were significantly higher than those of the control group (9.5 ± 1.7; p = 0.02 and p = 0.03, respectively). The residents' group's mean score (14.7 ± 1.4) was higher than the mean scores of the CDSS and Google groups (p = 0.01). Regarding common disease cases, the mean scores were 7.4 ± 0.7, 7.1 ± 0.7, and 8.2 ± 0.7 for the CDSS, Google, and residents' groups, respectively. There were no significant differences in mean scores (p = 0.1). CONCLUSIONS: Medical students who used the CDSS and Google were able to list differential diagnoses more accurately than those using neither. Furthermore, they could make the same level of differential diagnoses as residents in the context of common diseases. TRIAL REGISTRATION: This study was retrospectively registered with the University Hospital Medical Information Network Clinical Trials Registry on 24/12/2020 (unique trial number: UMIN000042831).

UNSTRUCTURED<p>ChatGPT (Open AI, San Francisco, California, USA) has gained considerable attention because of its natural and intuitive responses. One limitation of OpenAI is its failure to perform reinforcement learning based on reliable information, thereby providing inaccurate or meaningless answers. Fortunately, on March 2023 update introduced GPT-4, which, according to internal evaluations, is expected to increase the likelihood of producing factual responses by 40% compared with its predecessor, GPT-3.5. We verified the accuracy of ChatGPT based on GPT-4 (ChatGPT4) and based on GPT-3.5 (ChatGPT3.5) by solving the Japanese National Medical Examination. We excluded questions containing figures and tables unsupported by ChatGPT. Of the 400 questions, 292 were analyzed. The correct response rate for ChatGPT4 was 81.5%, which was significantly higher than 42.8%, the rate for ChatGPT3.5. Moreover, ChatGPT4 surpassed the passing standard (&gt;72%) for the Japanese National Medical Examination, indicating its potential as a diagnostic and therapeutic decision aid for physicians. We anticipate that future updates of ChatGPT will further enhance its accuracy, making it an invaluable resource in the field of medicine.</p>

We describe a case of pubic osteomyelitis in a 17-year-old Japanese male. The patient presented with acute left groin pain and left lower quadrant pain. He was evaluated at another hospital where pelvic X-ray/computed tomography was normal, and laboratory testing revealed only high C-reactive protein. Pelvic magnetic resonance imaging (MRI) on day three showed inflammation of the pubic attachment of the rectus abdominis muscle. Furthermore, a pelvic MRI performed 10 days after onset revealed a high signal on T2 short-TI inversion recovery in the left pubic bone, which was not found in the previous MRI, leading to a diagnosis of left pubic osteomyelitis. Symptoms improved rapidly after antibiotic therapy, and treatment was completed after six weeks. When a young athlete presents with fever and acute inguinal pain, osteomyelitis of the pubic bone should be considered as a differential diagnosis. This case report emphasizes the importance of taking a sports history during the interview and performing a repeat MRI for the early diagnosis of osteomyelitis of the pubic bone.

UNLABELLED: Angina bullosa haemorrhagica (ABH) is a disease of unknown cause that occurs most frequently in middle-aged and older adults and is characterized by the destruction of blood vessels in the submucosal layer of the middle pharynx and larynx centred on the soft palate, resulting in the formation of haemorrhagic blisters. It usually resolves within a day and heals without scarring within about a week. No treatment is necessary. However, cases of airway obstruction due to haematemesis have been reported, and this potential risk should be considered when tracheal intubation or upper gastrointestinal endoscopy is being performed. In this report, we describe the case of a 50-year-old man who developed a haematoma in the pharynx following upper endoscopy, which spontaneously ruptured and healed, leading to the diagnosis of ABH. The main purpose of this case report is to remind the reader that ABH improves without treatment, thus eliminating the need for unnecessary examination, and that there is a risk of airway obstruction depending on the site of the lesion. LEARNING POINTS: The key to the diagnosis of angina bullosa haemorrhagica (ABH) is a history of acute haemorrhagic vesicles caused by an external stimulus such as food or intubation, which resolve without scarring within a week or so.ABH can occur at any oropharyngeal site, but its occurrence in the pharyngeal region raises the risk of airway obstruction due to haematemesis.

This study aims to compare the effectiveness of Hybrid and Pure problem-based learning (PBL) in teaching clinical reasoning skills to medical students. The study sample consisted of 99 medical students participating in a clerkship rotation at the Department of General Medicine, Chiba University Hospital. They were randomly assigned to Hybrid PBL (intervention group, n = 52) or Pure PBL group (control group, n = 47). The quantitative outcomes were measured with the students' perceived competence in PBL, satisfaction with sessions, and self-evaluation of competency in clinical reasoning. The qualitative component consisted of a content analysis on the benefits of learning clinical reasoning using Hybrid PBL. There was no significant difference between intervention and control groups in the five students' perceived competence and satisfaction with sessions. In two-way repeated measure analysis of variance, self-evaluation of competency in clinical reasoning was significantly improved in the intervention group in "recalling appropriate differential diagnosis from patient's chief complaint" (F(1,97) = 5.295, p = 0.024) and "practicing the appropriate clinical reasoning process" (F(1,97) = 4.016, p = 0.038). According to multiple comparisons, the scores of "recalling appropriate history, physical examination, and tests on clinical hypothesis generation" (F(1,97) = 6.796, p = 0.011), "verbalizing and reflecting appropriately on own mistakes," (F(1,97) = 4.352, p = 0.040) "selecting keywords from the whole aspect of the patient," (F(1,97) = 5.607, p = 0.020) and "examining the patient while visualizing his/her daily life" (F(1,97) = 7.120, p = 0.009) were significantly higher in the control group. In the content analysis, 13 advantage categories of Hybrid PBL were extracted. In the subcategories, "acquisition of knowledge" was the most frequent subcategory, followed by "leading the discussion," "smooth discussion," "getting feedback," "timely feedback," and "supporting the clinical reasoning process." Hybrid PBL can help acquire practical knowledge and deepen understanding of clinical reasoning, whereas Pure PBL can improve several important skills such as verbalizing and reflecting on one's own errors and selecting appropriate keywords from the whole aspect of the patient.

BACKGROUND: Physicians frequently experience patients as difficult. Our study explores whether more empathetic physicians experience fewer patient encounters as difficult. OBJECTIVE: To investigate the association between physician empathy and difficult patient encounters (DPEs). DESIGN: Cross-sectional study. PARTICIPANTS: Participants were 18 generalist physicians with 3-8 years of experience. The investigation was conducted from August-September 2018 and April-May 2019 at six healthcare facilities. MAIN MEASURES: Based on the Jefferson Scale of Empathy (JSE) scores, we classified physicians into low and high empathy groups. The physicians completed the Difficult Doctor-Patient Relationship Questionnaire-10 (DDPRQ-10) after each patient visit. Scores ≥ 31 on the DDPRQ-10 indicated DPEs. We implemented multilevel mixed-effects logistic regression models to examine the association between physicians' empathy and DPE, adjusting for patient-level covariates (age, sex, history of mental disorders) and with physician-level clustering. KEY RESULTS: The median JSE score was 114 (range: 96-126), and physicians with JSE scores 96-113 and 114-126 were assigned to low and high empathy groups, respectively (n = 8 and 10 each); 240 and 344 patients were examined by physicians in the low and high empathy groups, respectively. Among low empathy physicians, 23% of encounters were considered difficulty, compared to 11% among high empathy groups (OR: 0.37; 95% CI = 0.19-0.72, p = 0.004). JSE scores and DDPRQ-10 scores were negatively correlated (r = -0.22, p < 0.01). CONCLUSION: Empathetic physicians were less likely to experience encounters as difficult. Empathy appears to be an important component of physician perception of encounter difficulty.

Objective: To determine if inter-disease distances between word embedding vectors using the picot-and-cluster strategy (PCS) are a valid quantitative representation of similar disease groups in a limited domain.Materials and Methods: Abstracts were extracted from the Ichushi-Web database and subjected to morphological analysis and training using the Word2Vec. From this, word embedding vectors were obtained. For words including "infarction", we calculated the cophenetic correlation coefficient (CCC) as an internal validity measure and the adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) with ICD-10 codes as the external validity measures. This was performed for each combination of metric and hierarchical clustering method.Results: Seventy-one words included "infarction", of which 38 diseases matched the ICD-10 standard with the appearance of 21 unique ICD-10 codes. The CCC was most significant at 0.8690 (metric and method: euclidean and centroid), while the AMI was maximal at 0.4109 (metric and method: cosine and correlation, and average and weighted). The NMI and ARI were maximal at 0.8463 and 0.3593, respectively (metric and method: cosine and complete).Discussion: The metric and method that maximized the internal validity measure were different from those that maximized the external validity measures; both produced different results. The Cosine distance should be used when considering ICD-10, and the Euclidean distance when considering the frequency of word occurrence.Conclusion: The distributed representation, when trained by Word2Vec on the "infarction" domain from a Japanese academic corpus, provides an objective inter-disease distance used in PCS.

OBJECTIVE: To clarify the factors associated with prolonged hospital stays, focusing on the COMplexity PRediction Instrument (COMPRI) score's accuracy in predicting the length of stay of newly hospitalised patients in general internal medicine wards. DESIGN: A case-control study. SETTING: Three general internal medicine wards in Chiba Prefecture, Japan. PARTICIPANTS: Thirty-four newly hospitalised patients were recruited between November 2017 and December 2019, with a final analytic sample of 33 patients. We included hospitals in different cities with general medicine outpatient and ward facilities, who agreed to participate. We excluded any patients who were re-hospitalised within 2 weeks of a prior discharge. PRIMARY AND SECONDARY OUTCOME MEASURES: Patients' COMPRI scores and their consequent lengths of hospital stay. RESULTS: The 17 patients (52%) allocated to the long-term hospitalisation group (those hospitalised ≥14 days) had a significantly higher average age, COMPRI score and percentage of participants with comorbid chronic illnesses than the short-term hospitalisation group (<14 days). A logistic regression model (model A, comprising only the COMPRI score as the explanatory variable) and a multiple logistic regression model (model B, comprising variables other than the COMPRI score as explanatory variables) were created as prediction models for the long-term hospitalisation group. When age ≥75 years, a COMPRI score ≥6 and a physician with 10 years' experience were set as explanatory variables, model A showed better predictive accuracy compared with model B (fivefold cross-validation, area under curve of 0.87 vs 0.78). The OR of a patient with a COMPRI score of ≥6 joining the long-term hospitalisation group was 4.25 (95% CI=1.43 to 12.63). CONCLUSIONS: Clinicians can use the COMPRI score when screening for complexity assessment to identify hospitalised patients at high risk of prolonged hospitalisation. Providing such patients with multifaceted and intensive care may shorten hospital stays.

Deep tendon reflexes (DTR) are a prerequisite skill in clinical clerkships. However, many medical students are not confident in their technique and need to be effectively trained. We evaluated the effectiveness of a flipped classroom for teaching DTR skills. We recruited 83 fifth-year medical students who participated in a clinical clerkship at the Department of General Medicine, Chiba University Hospital, from November 2018 to July 2019. They were allocated to the flipped classroom technique (intervention group, n = 39) or the traditional technique instruction group (control group, n = 44). Before procedural teaching, while the intervention group learned about DTR by e-learning, the control group did so face-to-face. A 5-point Likert scale was used to evaluate self-confidence in DTR examination before and after the procedural teaching (1 = no confidence, 5 = confidence). We evaluated the mastery of techniques after procedural teaching using the Direct Observation of Procedural Skills (DOPS). Unpaired t-test was used to analyze the difference between the two groups on the 5-point Likert scale and DOPS. We assessed self-confidence in DTR examination before and after procedural teaching using a free description questionnaire in the two groups. Additionally, in the intervention group, focus group interviews (FGI) (7 groups, n = 39) were conducted to assess the effectiveness of the flipped classroom after procedural teaching. Pre-test self-confidence in the DTR examination was significantly higher in the intervention group than in the control group (2.8 vs. 2.3, P = 0.005). Post-test self-confidence in the DTR examination was not significantly different between the two groups (3.9 vs. 4.1, P = 0.31), and so was mastery (4.3 vs. 4.1, P = 0.68). The questionnaires before the procedural teaching revealed themes common to the two groups, including "lack of knowledge" and "lack of self-confidence." Themes about prior learning, including "acquisition of knowledge" and "promoting understanding," were specific in the intervention group. The FGI revealed themes including "application of knowledge," "improvement in DTR technique," and "increased self-confidence." Based on these results, teaching DTR skills to medical students in flipped classrooms improves readiness for learning and increases self-confidence in performing the procedure at a point before procedural teaching.

During clinical reasoning case conferences, a learner-centered approach using teleconferencing can create a psychologically safe environment and help learners speak up. This study aims to measure the psychological safety of students who are supposed to self-explain their clinical reasoning to conference participants. This crossover study compared the effects of two clinical reasoning case conference methods on medical students' psychological safety. The study population comprised 4th-5th year medical students participating in a two-week general medicine clinical clerkship rotation, from September 2019 to February 2020. They participated in both a learner-centered approach teleconference and a traditional, live-style conference. Teleconferences were conducted in a separate room, with only a group of students and one facilitator. Participants in group 1 received a learner-centered teleconference in the first week and a traditional, live-style conference in the second week. Participants assigned to group 2 received a traditional, live-style conference in the first week and a learner-centered approach teleconference in the second week. After each conference, Edmondson's Psychological Safety Scale was used to assess the students' psychological safety. We also counted the number of students who self-explained their clinical reasoning processes during each conference. Of the 38 students, 34 completed the study. Six out of the seven psychological safety items were significantly higher in the learner-centered approach teleconferences (p<0.01). Twenty-nine (85.3%) students performed self-explanation in the teleconference compared to ten (29.4%) in the live conference (p<0.01). A learner-centered approach teleconference could improve psychological safety in novice learners and increase the frequency of their self-explanation, helping educators better assess their understanding. Based on these results, a learner-centered teleconference approach has the potential to be a method for teaching clinical reasoning to medical students.

Objective This case series aimed to investigate the clinical and pathological characteristics of persistent postural perceptual dizziness (PPPD). Methods We retrospectively examined the medical records of patients with chronic dizziness in our department, and tracked the percentage of PPPD, the age and sex, disorder duration, exacerbating factors for dizziness, and duration of momentary worsening dizziness. We also examined the duration of momentary worsening dizziness in cases of depression, anxiety disorder, and somatic symptom disorder. Results Among 229 patients with chronic dizziness, 14.4% (33/229) met the diagnostic criteria for PPPD. PPPD was the second most common disorder of patients with chronic dizziness after depression. The median age of patients with PPPD was 75 (75.8% female) and the median duration of the disorder was 60 months (range: 3-360 months). The exacerbating factors were motion without regard to direction or position (90.9%), upright posture (66.7%), and exposure to moving visual stimuli or complex visual patterns (30.3%). While the duration of momentary worsening dizziness was less than 10 minutes in 93.9% of patients with PPPD, the duration in patients with depression, anxiety disorder, and somatic symptom disorder were 3.6 % (2/55), 16.1% (5/31), and 0% (0/11), respectively. When the duration was less than 10 minutes, the odds ratios of PPPD for depression and anxiety disorder were 46.5 (95% CI: 6.1-362.0) and 40.3 (95% CI: 7.4-219.3), respectively. Conclusion Short episodes of momentary worsening dizziness constitute a distinctive feature of PPPD that may be useful for differentiating PPPD from other types of psychogenic dizziness.