Medical residents’ attitude toward professionalism and assessment of their professional behaviors: a cross-sectional survey

Background: Professionalism training is a core component of medical education. This study’s aim was to determine medical residents’ attitudes toward professionalism and self-reported professional behaviors. Methods: In a cross-sectional survey at Qazvin University of Medical Sciences, 100 medical residents in their first through third years were invited to participate in a survey between April and June of 2015. Participants responded to a written questionnaire consisting of 7 demographic and 42 content items in 4 domains. Content items were rated on a 5-point Likert scale. Results with a mean of less than three were considered undesirable. A non-parametric Kruskal-Wallis test was used to compare distributions in the study groups. All statistical analyses were performed using SPSS version 22. Results: With a response rate of 87%, a mean age of 31.9 (SD: 3.0) was recorded. The mean Likert score for the perception of residents on the ethical importance of “colleague report” and “reporting error” was undesirable. The percentage of residents’ self-reported unprofessional behaviors during their training was high. Moreover, 71% (95% CI: 61-80) of residents believed that ethics should be formally taught in the medical school curriculum. Over 97% (95% CI: 94-100) believed that learning medical ethics and professionalism requires more than a theoretical course. A longitudinal approach was the most agreed-upon format. Conclusion: Based on the findings of this research, despite a relatively acceptable rate of professional behaviors, residents perceive a need for a more comprehensive curricular attention to practical ethics and ethically important professional development during residency training

CT ​EvaLuation ​by ​ARtificial ​Intelligence ​For ​Atherosclerosis, Stenosis and Vascular ​MorphologY ​(CLARIFY): ​A ​Multi-center, international study

Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.BACKGROUND: Atherosclerosis evaluation by coronary computed tomography angiography (CCTA) is promising for coronary artery disease (CAD) risk stratification, but time consuming and requires high expertise. Artificial Intelligence (AI) applied to CCTA for comprehensive CAD assessment may overcome these limitations. We hypothesized AI aided analysis allows for rapid, accurate evaluation of vessel morphology and stenosis. METHODS: This was a multi-site study of 232 patients undergoing CCTA. Studies were analyzed by FDA-cleared software service that performs AI-driven coronary artery segmentation and labeling, lumen and vessel wall determination, plaque quantification and characterization with comparison to ground truth of consensus by three L3 readers. CCTAs were analyzed for: % maximal diameter stenosis, plaque volume and composition, presence of high-risk plaque and Coronary Artery Disease Reporting & Data System (CAD-RADS) category. RESULTS: AI performance was excellent for accuracy, sensitivity, specificity, positive predictive value and negative predictive value as follows: >70% stenosis: 99.7%, 90.9%, 99.8%, 93.3%, 99.9%, respectively; >50% stenosis: 94.8%, 80.0%, 97.0, 80.0%, 97.0%, respectively. Bland-Altman plots depict agreement between expert reader and AI determined maximal diameter stenosis for per-vessel (mean difference -0.8%; 95% CI 13.8% to -15.3%) and per-patient (mean difference -2.3%; 95% CI 15.8% to -20.4%). L3 and AI agreed within one CAD-RADS category in 228/232 (98.3%) exams per-patient and 923/924 (99.9%) vessels on a per-vessel basis. There was a wide range of atherosclerosis in the coronary artery territories assessed by AI when stratified by CAD-RADS distribution. CONCLUSIONS: AI-aided approach to CCTA interpretation determines coronary stenosis and CAD-RADS category in close agreement with consensus of L3 expert readers. There was a wide range of atherosclerosis identified through AI.proofpublishe

a CLARIFY trial sub-study

Publisher Copyright: © 2022Background: The difference between expert level (L3) reader and artificial intelligence (AI) performance for quantifying coronary plaque and plaque components is unknown. Objective: This study evaluates the interobserver variability among expert readers for quantifying the volume of coronary plaque and plaque components on coronary computed tomographic angiography (CCTA) using an artificial intelligence enabled quantitative CCTA analysis software as a reference (AI-QCT). Methods: This study uses CCTA imaging obtained from 232 patients enrolled in the CLARIFY (CT EvaLuation by ARtificial Intelligence For Atherosclerosis, Stenosis and Vascular MorphologY) study. Readers quantified overall plaque volume and the % breakdown of noncalcified plaque (NCP) and calcified plaque (CP) on a per vessel basis. Readers categorized high risk plaque (HRP) based on the presence of low-attenuation-noncalcified plaque (LA-NCP) and positive remodeling (PR; ≥1.10). All CCTAs were analyzed by an FDA-cleared software service that performs AI-driven plaque characterization and quantification (AI-QCT) for comparison to L3 readers. Reader generated analyses were compared among readers and to AI-QCT generated analyses. Results: When evaluating plaque volume on a per vessel basis, expert readers achieved moderate to high interobserver consistency with an intra-class correlation coefficient of 0.78 for a single reader score and 0.91 for mean scores. There was a moderate trend between readers 1, 2, and 3 and AI with spearman coefficients of 0.70, 0.68 and 0.74, respectively. There was high discordance between readers and AI plaque component analyses. When quantifying %NCP v. %CP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.23, 0.34 and 0.24, respectively, compared to AI with a spearman coefficient of 0.38, 0.51, and 0.60, respectively. The intra-class correlation coefficient among readers for plaque composition assessment was 0.68. With respect to HRP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.22, 0.26, and 0.17, respectively, and a spearman coefficient of 0.36, 0.35, and 0.44, respectively. Conclusion: Expert readers performed moderately well quantifying total plaque volumes with high consistency. However, there was both significant interobserver variability and high discordance with AI-QCT when quantifying plaque composition.publishersversionpublishe

Interobserver Variability Among Expert Readers Quantifying Plaque Volume and Plaque Characteristics on Coronary CT Angiography: A CLARIFY Trial Sub-Study

Background: The difference between expert level (L3) reader and artificial intelligence (AI) performance for quantifying coronary plaque and plaque components is unknown. Objective: This study evaluates the interobserver variability among expert readers for quantifying the volume of coronary plaque and plaque components on coronary computed tomographic angiography (CCTA) using an artificial intelligence enabled quantitative CCTA analysis software as a reference (AI-QCT). Methods: This study uses CCTA imaging obtained from 232 patients enrolled in the CLARIFY (CT EvaLuation by ARtificial Intelligence For Atherosclerosis, Stenosis and Vascular MorphologY) study. Readers quantified overall plaque volume and the % breakdown of noncalcified plaque (NCP) and calcified plaque (CP) on a per vessel basis. Readers categorized high risk plaque (HRP) based on the presence of low-attenuation-noncalcified plaque (LA-NCP) and positive remodeling (PR; ≥1.10). All CCTAs were analyzed by an FDA-cleared software service that performs AI-driven plaque characterization and quantification (AI-QCT) for comparison to L3 readers. Reader generated analyses were compared among readers and to AI-QCT generated analyses. Results: When evaluating plaque volume on a per vessel basis, expert readers achieved moderate to high interobserver consistency with an intra-class correlation coefficient of 0.78 for a single reader score and 0.91 for mean scores. There was a moderate trend between readers 1, 2, and 3 and AI with spearman coefficients of 0.70, 0.68 and 0.74, respectively. There was high discordance between readers and AI plaque component analyses. When quantifying %NCP v. %CP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.23, 0.34 and 0.24, respectively, compared to AI with a spearman coefficient of 0.38, 0.51, and 0.60, respectively. The intra-class correlation coefficient among readers for plaque composition assessment was 0.68. With respect to HRP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.22, 0.26, and 0.17, respectively, and a spearman coefficient of 0.36, 0.35, and 0.44, respectively. Conclusion: Expert readers performed moderately well quantifying total plaque volumes with high consistency. However, there was both significant interobserver variability and high discordance with AI-QCT when quantifying plaque composition

An investigation of awareness of professionalism in medical school students and residents of Qazvin university of medical sciences

Abstract: Introduction: Professionalism is a multidimensional concept and worthy issue that concludes social expectations to use science, technical skills, clinical reasoning, emotions, values and feed back to daily actions. In order to reach predetermined goals which is necessary for medical professionalism to entice the confidence of the society and it is a valuable factor of correlation. This research is done in order to determine the level of acquaintance with professionalism among the learners of Shahid Babaee medical faculty at Qazvin University of medical sciences. Methodology: Findings have shown that there is a meaningful statistical difference between different level of learners in: Ignoring racial and sexual differences in medical care, Giving care without considering the ability to pay from the patient, Adherence to social principles, capability of self-adjustment and self-forming and engagement with health team , Awareness of team work and observing principles of altruism in dealing with patient, Honesty, Reliability and secrecy, training professional skills and requesting different kinds of theoretical and practical training. Learners have reported the quality of professors’ role modeling and the departmental professionalism training is low at level in some fields. Discussion and conclusion: According to the findings strength and lack of professionalism that are determined, some efforts should be done to correct the weak points and improve the strength. There have been great differences such as sexual differences, doing necessary medical care without considering the ability to pay by patient between interns and other learners which needs great attention as the first line in facing patients in a medical and educational center. Due to the importance of professionalism in improving services, professionalism training and considering the terms of standards in professionalism at learners’ evaluation, professors promote and medical faculties ranking, seems to be necessary. Keywords: ethic, professionalism in medicine, role mode

Prospects for Manipulation of Mesenchymal Stem Cells in Tumor Therapy: Anti-Angiogenesis Property on the Spotlight

The interactions between the tumor microenvironment and the tumor cells confers a condition that accelerate or decelerate the development of tumor. Of these cells, mesenchymal stem cells (MSCs) have the potential to modulate the tumor cells. MSCs have been established with double functions, whereby contribute to a tumorigenic or anti-tumor setting. Clinical studies have indicated the potential of MSCs to be used as tool in treating the human cancer cells. One of the advantageous features of MSCs that make them as a well-suited tool for cancer therapy is the natural tumor-trophic migration potential. A key specification of the tumor development has been stablished to be angiogenesis. As a result, manipulation of angiogenesis has become an attractive approach for cancer therapy. This review Article will seek to clarify the anti-angiogenesis strategy in modulating the MSCs to treat the tumor cells

Impact of atherosclerosis imaging-quantitative computed tomography on diagnostic certainty, downstream testing, coronary revascularization, and medical therapy: the CERTAIN study.

AIMS: The incremental impact of atherosclerosis imaging-quantitative computed tomography (AI-QCT) on diagnostic certainty and downstream patient management is not yet known. The aim of this study was to compare the clinical utility of the routine implementation of AI-QCT versus conventional visual coronary CT angiography (CCTA) interpretation. METHODS AND RESULTS: In this multi-centre cross-over study in 5 expert CCTA sites, 750 consecutive adult patients referred for CCTA were prospectively recruited. Blinded to the AI-QCT analysis, site physicians established patient diagnoses and plans for downstream non-invasive testing, coronary intervention, and medication management based on the conventional site assessment. Next, physicians were asked to repeat their assessments based upon AI-QCT results. The included patients had an age of 63.8 ± 12.2 years; 433 (57.7%) were male. Compared with the conventional site CCTA evaluation, AI-QCT analysis improved physicians confidence two- to five-fold at every step of the care pathway and was associated with change in diagnosis or management in the majority of patients (428; 57.1%; P < 0.001), including for measures such as Coronary Artery Disease-Reporting and Data System (CAD-RADS) (295; 39.3%; P < 0.001) and plaque burden (197; 26.3%; P < 0.001). After AI-QCT including ischaemia assessment, the need for downstream non-invasive and invasive testing was reduced by 37.1% (P < 0.001), compared with the conventional site CCTA evaluation. Incremental to the site CCTA evaluation alone, AI-QCT resulted in statin initiation/increase an aspirin initiation in an additional 28.1% (P < 0.001) and 23.0% (P < 0.001) of patients, respectively. CONCLUSION: The use of AI-QCT improves diagnostic certainty and may result in reduced downstream need for non-invasive testing and increased rates of preventive medical therapy