塚本 知子

BACKGROUND: Generative artificial intelligence (GAI), represented by large language models, have the potential to transform health care and medical education. In particular, GAI's impact on higher education has the potential to change students' learning experience as well as faculty's teaching. However, concerns have been raised about ethical consideration and decreased reliability of the existing examinations. Furthermore, in medical education, curriculum reform is required to adapt to the revolutionary changes brought about by the integration of GAI into medical practice and research. OBJECTIVE: This study analyzes the impact of GAI on medical education curricula and explores strategies for adaptation. METHODS: The study was conducted in the context of faculty development at a medical school in Japan. A workshop involving faculty and students was organized, and participants were divided into groups to address two research questions: (1) How does GAI affect undergraduate medical education curricula? and (2) How should medical school curricula be reformed to address the impact of GAI? The strength, weakness, opportunity, and threat (SWOT) framework was used, and cross-SWOT matrix analysis was used to devise strategies. Further, 4 researchers conducted content analysis on the data generated during the workshop discussions. RESULTS: The data were collected from 8 groups comprising 55 participants. Further, 5 themes about the impact of GAI on medical education curricula emerged: improvement of teaching and learning, improved access to information, inhibition of existing learning processes, problems in GAI, and changes in physicians' professionality. Positive impacts included enhanced teaching and learning efficiency and improved access to information, whereas negative impacts included concerns about reduced independent thinking and the adaptability of existing assessment methods. Further, GAI was perceived to change the nature of physicians' expertise. Three themes emerged from the cross-SWOT analysis for curriculum reform: (1) learning about GAI, (2) learning with GAI, and (3) learning aside from GAI. Participants recommended incorporating GAI literacy, ethical considerations, and compliance into the curriculum. Learning with GAI involved improving learning efficiency, supporting information gathering and dissemination, and facilitating patient involvement. Learning aside from GAI emphasized maintaining GAI-free learning processes, fostering higher cognitive domains of learning, and introducing more communication exercises. CONCLUSIONS: This study highlights the profound impact of GAI on medical education curricula and provides insights into curriculum reform strategies. Participants recognized the need for GAI literacy, ethical education, and adaptive learning. Further, GAI was recognized as a tool that can enhance efficiency and involve patients in education. The study also suggests that medical education should focus on competencies that GAI hardly replaces, such as clinical experience and communication. Notably, involving both faculty and students in curriculum reform discussions fosters a sense of ownership and ensures broader perspectives are encompassed.

BACKGROUND: The gamification of learning increases student enjoyment, and motivation and engagement in learning tasks. This study investigated the effects of gamification using decision-making cards (DMCs) on diagnostic decision-making and cost using case scenarios. METHOD: Thirty medical students in clinical clerkship participated and were randomly assigned to 14 small groups of 2-3 medical students each. Decision-making was gamified using DMCs with a clinical information heading and medical cost on the front, and clinical information details on the back. First, each team was provided with brief clinical information on case scenarios. Subsequently, DMCs depending on the case were distributed to each team, and team members chose cards one at a time until they reached a diagnosis of the case. The total medical cost was then scored based on the number and contents of cards drawn. Four case scenarios were conducted. The quantitative outcomes including confidence in effective clinical decision-making, motivation to learn diagnostic decision-making, and awareness of medical costs were measured before and after our gamification by self-evaluation using a 7-point Likert scale. The qualitative component consisted of a content analysis on the benefits of learning clinical reasoning using DMCs. RESULT: Confidence in effective clinical decision-making, motivation to learn diagnostic decision-making, and awareness of medical cost were significantly higher after the gamification. Furthermore, comparing the clinical case scenario tackled last with the one tackled first, the average medical cost of all cards drawn by students decreased significantly from 11,921 to 8,895 Japanese yen. In the content analysis, seven advantage categories of DMCs corresponding to clinical reasoning components were extracted (information gathering, hypothesis generation, problem representation, differential diagnosis, leading or working diagnosis, diagnostic justification, and management and treatment). CONCLUSION: Teaching medical students clinical reasoning using DMCs can improve clinical decision-making confidence and learning motivation, and reduces medical cost in clinical case scenarios. In addition, it can help students to acquire practical knowledge, deepens their understanding of clinical reasoning, and identifies several important clinical reasoning skills including diagnostic decision-making and awareness of medical costs. Gamification using DMCs can be an effective teaching method for improving medical students' diagnostic decision-making and reducing costs.

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).

BACKGROUND: To investigate whether speech recognition software for generating interview transcripts can provide more specific and precise feedback for evaluating medical interviews. METHODS: The effects of the two feedback methods on student performance in medical interviews were compared using a prospective observational trial. Seventy-nine medical students in a clinical clerkship were assigned to receive either speech-recognition feedback (n = 39; SRS feedback group) or voice-recording feedback (n = 40; IC recorder feedback group). All students' medical interviewing skills during mock patient encounters were assessed twice, first using a mini-clinical evaluation exercise (mini-CEX) and then a checklist. Medical students then made the most appropriate diagnoses based on medical interviews. The diagnostic accuracy, mini-CEX, and checklist scores of the two groups were compared. RESULTS: According to the study results, the mean diagnostic accuracy rate (SRS feedback group:1st mock 51.3%, 2nd mock 89.7%; IC recorder feedback group, 57.5%-67.5%; F(1, 77) = 4.0; p = 0.049), mini-CEX scores for overall clinical competence (SRS feedback group: 1st mock 5.2 ± 1.1, 2nd mock 7.4 ± 0.9; IC recorder feedback group: 1st mock 5.6 ± 1.4, 2nd mock 6.1 ± 1.2; F(1, 77) = 35.7; p < 0.001), and checklist scores for clinical performance (SRS feedback group: 1st mock 12.2 ± 2.4, 2nd mock 16.1 ± 1.7; IC recorder feedback group: 1st mock 13.1 ± 2.5, 2nd mock 13.8 ± 2.6; F(1, 77) = 26.1; p < 0.001) were higher with speech recognition-based feedback. CONCLUSIONS: Speech-recognition-based feedback leads to higher diagnostic accuracy rates and higher mini-CEX and checklist scores. TRIAL REGISTRATION: This study was registered in the Japan Registry of Clinical Trials on June 14, 2022. Due to our misunderstanding of the trial registration requirements, we registered the trial retrospectively. This study was registered in the Japan Registry of Clinical Trials on 7/7/2022 (Clinical trial registration number: jRCT1030220188).

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.

A 66-year-old British man was referred to our department because of a 2.5 year history of bilateral paroxysmal weakness of the lower limbs. It occurred when he walked for about 10 minutes, so he would stop in place and spontaneously rest for up to 15 minutes. When carbamazepine 200 mg/day was administered, the severity and frequency of the symptoms reduced by half and resolved when the dose was increased to 300 mg/day. Even if no involuntary movement is observed, paroxysmal exercise-induced dyskinesia should be considered in patients with paroxysmal painless gait disturbance, and a therapeutic trial of anticonvulsants may be helpful.

Background: The supply of primary care physicians is associated with better health outcomes and a lower total cost of health services. However, the effect of the presence or absence of primary care physicians on health-related quality of life (QOL) is unknown. We comparatively investigated the health-related QOL of ordinary citizens according to the presence or absence of a primary care physician.Methods: We conducted an observational cross-sectional study using a propensity score analysis. A questionnaire on health-related QOL (SF-36v2, age, gender, presence or absence of a primary care physician, and chronic disease status) was mailed to 2200 individuals identified through stratified random sampling. We used propensity scores to compensate for covariates and analyzed three component SF-36 summary scores and subscale scores of the "primary care physician" and "no primary care physician" groups.Results: Valid responses were received from 1095 individuals (49.8%). The "primary care physician group" comprised 653 individuals (59.6%). The physical health component scores of the "primary care physician group" were significantly lower than those of the "no primary care physician group," and the "mental health component" scores were significantly higher (p = 0.032, p = 0.009). For the subscales, scores for "vitality" and "mental health" were significantly higher in the "primary care physician group" (p = 0.014, p = 0.018).Conclusions Patients who had a primary care physician with whom they could comfortably consult at any time had a high mental health component score, and low physical health component score in the health-related QOL.

BACKGROUND: The COVID-19 pandemic has created a need for educational materials and methods that can replace clinical clerkships (CCs) for online simulated clinical practice (online-sCP). This study evaluates the impact of using simulated electronic health records (sEHR) for inpatients, and electronic problem-based learning (e-PBL) and online virtual medical interviews (online-VMI) for outpatients, for an online-sCP using a learning management system (LMS) and online meeting system facilitated by a supervising physician. METHODS: The sEHR was reviewed by medical students and subsequently discussed with a supervising physician using an online meeting system. In the e-PBL, medical students reviewed the simulated patients and discussed on the LMS. For the online-VMI, a faculty member acted as an outpatient and a student acted as the doctor. Small groups of students discussed the clinical reasoning process using the online meeting system. A mixed-method design was implemented. Medical students self-assessed their clinical competence before and after the online-sCP. They answered questionnaires and participated in semi-structured focus group interviews (FGIs) regarding the advantages and disadvantages of the practice. RESULTS: Forty-three students completed the online-sCP during May and June 2020. All students indicated significant improvement in all aspects of self-evaluation of clinical performance after the online-sCP. Students using sEHR reported significant improvement in writing daily medical records and medical summaries. Students using e-PBL and online-VMI reported significant improvement in medical interviews and counseling. Students also indicated CCs as more useful for learning associated with medical interviews, physical examinations, and humanistic qualities like professionalism than the online-sCP. Eight FGIs were conducted (n = 42). The advantages of online-sCP were segregated into five categories (learning environment, efficiency, accessibility, self-paced learning, and interactivity); meanwhile, the disadvantages of online-sCP were classified into seven categories (clinical practice experience, learning environment, interactivity, motivation, memory retention, accessibility, and extraneous cognitive load). CONCLUSIONS: Online-sCP with sEHR, e-PBL, and online-VMI could be useful in learning some of the clinical skills acquired through CC. These methods can be implemented with limited preparation and resources.

BACKGROUND: Patient awareness surveys on polypharmacy have been reported previously, but no previous study has examined the effects of sending feedback to health professionals on reducing medication use. Our study aimed to conduct a patient survey to examine factors contributing to polypharmacy, feedback the results to health professionals, and analyze the resulting changes in the number of polypharmacy patients and prescribed medications. METHODS: After conducting a questionnaire survey of patients in Study 1, we provided its results to the healthcare professionals, and then surveyed the number of polypharmacy patients and oral medications using a before-after comparative study design in Study 2. In Study 1, we examined polypharmacy and its contributing factors by performing logistic regression analysis. In Study 2, we performed a t-test and a chi-square test. RESULTS: In the questionnaire survey, significant differences were found in the following 3 items: age (odds ratio (OR) = 3.14; 95% confidence interval (CI) = 2.01-4.91), number of medical institutions (OR = 2.34; 95%CI = 1.50-3.64), and patients' difficulty with asking their doctors to deprescribe their medications (OR = 2.21; 95%CI = 1.25-3.90). After the feedback, the number of polypharmacy patients decreased from 175 to 159 individuals and the mean number of prescribed medications per patient decreased from 8.2 to 7.7 (p < 0.001, respectively). CONCLUSIONS: Providing feedback to health professionals on polypharmacy survey results may lead to a decrease in the number of polypharmacy patients. Factors contributing to polypharmacy included age (75 years or older), the number of medical institutions (2 or more institutions), and patients' difficulty with asking their physicians to deprescribe their medications. Feedback to health professionals reduced the percentage of polypharmacy patients and the number of prescribed medications. TRIAL REGISTRATION: UMIN. Registered 21 June 2020 - Retrospectively registered, https://www.umin.ac.jp/ctr/index-j.htm.

Background: The supply of primary care physicians is associated with better health outcomes and a lower total cost of health services. However, the effect of the presence or absence of primary care physicians on health-related quality of life (QOL) is unknown. We comparatively investigated the health-related QOL of ordinary citizens according to the presence or absence of a primary care physician. Methods: We conducted an observational cross-sectional study using a propensity score analysis. A questionnaire on health-related QOL (SF-36v2, age, gender, presence or absence of a primary care physician, and chronic disease status) was mailed to 2200 individuals identified through stratified random sampling. We used propensity scores to compensate for covariates and analyzed three component SF-36 summary scores and subscale scores of the “primary care physician” and “no primary care physician” groups. Results: Valid responses were received from 1095 individuals (49.8%). The “primary care physician group” comprised 653 individuals (59.6%). The physical health component scores of the “primary care physician group” were significantly lower than those of the “no primary care physician group,” and the “mental health component” scores were significantly higher (p = 0.032, p = 0.009). For the subscales, scores for “vitality” and “mental health” were significantly higher in the “primary care physician group” (p = 0.014, p = 0.018). Conclusions: Patients who had a primary care physician with whom they could comfortably consult at any time had a high mental health component score, and low physical health component score in the health-related QOL.

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.

Purpose: Fundoscopic exams are conducted during outpatient consultations to assess intracranial disease and complications from chronic diseases. Low level of confidence in these skills which physicians have is one of the causes that implementation frequency is low. Research has not yet identified specific measures through which the healthcare system may increase the implementation of fundoscopic exams nor a qualitative process that enables physicians to gain confidence in their fundoscopic exam skills. We introduced a checklist and conducted a mixed-methods study. Methods: This study is a before-and-after study, within an embedded-experimental mixed-methods design. We sampled 15 physicians in the department of general medicine at a university hospital assigned to initial consultation. We introduced a checklist to verify whether the fundoscopic exam was implemented. Measures are implementation ratio of the fundoscopic exam to the total number of indication cases, and Visual Analog Scale (VAS) values for the confidence in procedure are measured before and after the intervention. We obtained qualitative data from interviews and used the Modified Grounded Theory Approach. Results: We observed significant increases in the implementation ratio (19.2% (29/151 cases) vs 64.8% (105/162 cases), p<0.001) and in the VAS value for the confidence (1.89 mm vs 4.68 mm (p<0.001)). Analysis of the interviews revealed the following. To increase the implementation, it is necessary to reduce the <Lack of confidence> and <Forgetting>, which is prevented by the checklist. The <Lack of successful experiences in detecting abnormalities> leads to a <Lack of confidence>. Repeated executions result in <Successful experiences and confidence building>. Conclusion: The intervention increased the implementation ratio, thereby increasing successful experiences and confidence among physicians. The growth of confidence boosted motivations to implement fundoscopic exams.

A 16-year-old boy who was a non-smoker presented with a prolonged severe dry cough and malaise of 3 months in duration. Despite an increase in the patient's inflammatory marker levels, no respiratory lesions were radiologically or serologically detected. We suspected that the cough reflex pathway had been stimulated by large vessel vasculitis (LVV, a non-respiratory inflammatory condition) and diagnosed the patient with Takayasu arteritis. While inflammation of either the ascending pharyngeal or pulmonary artery have been reported to cause cough in patients with LVV, the present case shows that intense inflammation of the aortic arch and the starting portion of its main branches may stimulate a vagus nerve branch as a novel mechanism causing cough.

BACKGROUND: This study aimed to clarify the influence of predicting a correct diagnosis from the history on physical examination by comparing the diagnostic accuracy of auscultation with and without clinical information. METHODS: The participants were 102 medical students from the 2013 clinical clerkship course. Auscultation was performed with a cardiology patient simulator. Participants were randomly assigned to two groups. Each group listened to a different simulated heart murmur and then made a diagnosis without clinical information. Next, a history suggesting a different murmur was provided to each group and they predicted the diagnosis. Finally, the students listened to a murmur corresponding to the history provided and again made a diagnosis. Correct and incorrect diagnosis rates of auscultation were compared between students with and without clinical information, between students predicting a correct or incorrect diagnosis from the history (correct and incorrect prediction groups, respectively), and between students without clinical information and those making an incorrect prediction. RESULTS: For auscultation with or without clinical information, the correct diagnosis rate was 62.7% (128/204 participants) versus 54.4% (111/204 participants), showing no significant difference (P=0.09). After receiving clinical information, a correct diagnosis was made by 102/117 students (87.2%) in the correct prediction group versus 26/87 students (29.9%) in the incorrect prediction group, showing a significant difference (P=0.006). The correct diagnosis rate was also significantly lower in the incorrect prediction group than when the students performed auscultation without clinical information (54.4% versus 29.9%, P<0.001). CONCLUSION: Obtaining a history alone does not improve the diagnostic accuracy of physical examination. However, accurately predicting the diagnosis from the history is associated with higher diagnostic accuracy of physical examination, while incorrect prediction is associated with lower diagnostic accuracy of examination.