Different Levels Of Expertise Research Articles

Assessment of a semiquantitative scoring system for mild-to-moderate gill lesions in Atlantic salmon reared in recirculating aquaculture systems in Norway.

Compromised gill health is a critical cause of forfeited welfare in Atlantic salmon farming. Detecting and quantifying the early onset of gill disease is important to reveal initial inflicting stimuli. We collected gill samples of 45 Atlantic salmon from 2 commercial recirculating aquaculture systems (RASs) spanning fry-to-market-size fish with no clinical signs of gill disease. Gill samples were assessed histologically by 3 independent raters with different levels of experience. Semiquantitative scoring for 7 types of gill changes was carried out for 10 filaments per gill (450 filaments total) over 3 rounds on anonymized samples. Scores were summarized for each type of gill change. The assumed clinical relevance for each change was transformed into a category score, followed by an assessment of agreement within (intra) and between (inter) raters. A generalized linear model estimated the difference in score levels between raters. For each rater, intra-rater agreement was high for 6 gill changes and moderate for 1 gill change. Inter-rater agreement was moderate to almost-perfect, except for 2 gill changes; generalized linear model regression revealed systematic differences in score usage between the raters. Our scoring protocol worked satisfactorily for mucous cell amount, lamellar clubbing, lamellar hypertrophy and/or hyperplasia, and aneurysms, despite different levels of expertise in histologic evaluation. Intra-rater agreement was consistent, but differences existed for interlamellar hypercellularity, lamellar inflammation, and degeneration. Scoring subclinical gill changes is a challenge, and our scoring system for mild-to-moderate lesions may enable early intervention to limit the detrimental effects of poor gill health in RAS farming.

Mass Casualty Incident Training in Immersive Virtual Reality: Quasi-Experimental Evaluation of Multimethod Performance Indicators.

Immersive virtual reality (iVR) has emerged as a training method to prepare medical first responders (MFRs) for mass casualty incidents (MCIs) and disasters in a resource-efficient, flexible, and safe manner. However, systematic evaluations and validations of potential performance indicators for virtual MCI training are still lacking. This study aimed to investigate whether different performance indicators based on visual attention, triage performance, and information transmission can be effectively extended to MCI training in iVR by testing if they can discriminate between different levels of expertise. Furthermore, the study examined the extent to which such objective indicators correlate with subjective performance assessments. A total of 76 participants (mean age 25.54, SD 6.01 y; 45/76, 59% male) with different medical expertise (MFRs: paramedics and emergency physicians; non-MFRs: medical students, in-hospital nurses, and other physicians) participated in 5 virtual MCI scenarios of varying complexity in a randomized order. Tasks involved assessing the situation, triaging virtual patients, and transmitting relevant information to a control center. Performance indicators included eye-tracking-based visual attention, triage accuracy, triage speed, information transmission efficiency, and self-assessment of performance. Expertise was determined based on the occupational group (39/76, 51% MFRs vs 37/76, 49% non-MFRs) and a knowledge test with patient vignettes. Triage accuracy (d=0.48), triage speed (d=0.42), and information transmission efficiency (d=1.13) differentiated significantly between MFRs and non-MFRs. In addition, higher triage accuracy was significantly associated with higher triage knowledge test scores (Spearman ρ=0.40). Visual attention was not significantly associated with expertise. Furthermore, subjective performance was not correlated with any other performance indicator. iVR-based MCI scenarios proved to be a valuable tool for assessing the performance of MFRs. The results suggest that iVR could be integrated into current MCI training curricula to provide frequent, objective, and potentially (partly) automated performance assessments in a controlled environment. In particular, performance indicators, such as triage accuracy, triage speed, and information transmission efficiency, capture multiple aspects of performance and are recommended for integration. While the examined visual attention indicators did not function as valid performance indicators in this study, future research could further explore visual attention in MCI training and examine other indicators, such as holistic gaze patterns. Overall, the results underscore the importance of integrating objective indicators to enhance trainers' feedback and provide trainees with guidance on evaluating and reflecting on their own performance.

Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation

We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, using a prospective cross-center, multi-level approach. We developed, implemented, and tested a deep-learning model for fetal growth scans using both retrospective and prospective data. We used a modified Progressive Concept Bottleneck Model with pre-established clinical concepts as explanations (feedback on image optimization and presence of anatomical landmarks) as well as segmentations (outlining anatomical landmarks). The model was evaluated prospectively by assessing the following: the model’s ability to assess standard plane quality, the correctness of explanations, the clinical usefulness of explanations, and the model’s ability to discriminate between different levels of expertise among clinicians. We used 9352 annotated images for model development and 100 videos for prospective evaluation. Overall classification accuracy was 96.3%. The model’s performance in assessing standard plane quality was on par with that of clinicians. Agreement between model segmentations and explanations provided by expert clinicians was found in 83.3% and 74.2% of cases, respectively. A panel of clinicians evaluated segmentations as useful in 72.4% of cases and explanations as useful in 75.0% of cases. Finally, the model reliably discriminated between the performances of clinicians with different levels of experience (p- values < 0.01 for all measures) Our study has successfully developed an Explainable AI model for real-time feedback to clinicians performing fetal growth scans. This work contributes to the existing literature by addressing the gap in the clinical validation of Explainable AI models within fetal medicine, emphasizing the importance of multi-level, cross-institutional, and prospective evaluation with clinician end-users. The prospective clinical validation uncovered challenges and opportunities that could not have been anticipated if we had only focused on retrospective development and validation, such as leveraging AI to gauge operator competence in fetal ultrasound.

Impact of team experience on robot-assisted surgery for rectal cancer: A comparative study.

The robotic surgical team in the operating room plays an important role in determining the outcome of a robotic approach. This study aimed to compare the outcomes of 2 hospitals with different levels of expertise in robot-assisted rectal cancer surgery. This retrospective study analyzed 195 patients who underwent robot-assisted rectal resection at 2 referral centers for the treatment of rectal cancer between March 2018 and December 2021. In total, 195 patients had undergone robotic rectal cancer surgery: 95 performed by an expert team, and 100 by a novel team. The expert team performed more low anterior resections (55.8%) than the novel team (33%) (P = .001), and the total operative time varied significantly between the groups (P < .001). The novel team's operative time was 135 minutes longer than the expert team's. The expert team had no conversions to open surgery, while the novel team had an 8% conversion rate (P = .007). In this study, overall morbidity was 45.3% among patients treated by the expert team versus 38% among those treated by the novice team (P = .304). Severe complications (Clavien-Dindo grade > IIIB) occurred at a rate of 10% in both groups. Incomplete mesorectal excision was observed in 3.2% of the expert team's patients versus 4.2% of the novice team's (P = .65). The expert team achieved a shorter operative time and less conversion to open surgery. However, the morbidity and pathological outcomes were comparable between the teams. The introduction of robotic surgery in a team with early-stage surgical experience was safe.

Validation of Artificial Intelligence Computer-Aided Detection of Colonic Neoplasm in Colonoscopy.

Controlling colonoscopic quality is important in the detection of colon polyps during colonoscopy as it reduces the overall long-term colorectal cancer risk. Artificial intelligence has recently been introduced in various medical fields. In this study, we aimed to validate a previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm called ALPHAON® and compare outcomes with previous studies that showed that AI outperformed and assisted endoscopists of diverse levels of expertise in detecting colon polyps. We used the retrospective data of 500 still images, including 100 polyp images and 400 healthy colon images. In addition, we validated the CADe algorithm and compared its diagnostic performance with that of two expert endoscopists and six trainees from Gachon University Gil Medical Center. After a washing-out period of over 2 weeks, endoscopists performed polyp detection on the same dataset with the assistance of ALPHAON®. The CADe algorithm presented a high capability in detecting colon polyps, with an accuracy of 0.97 (95% CI: 0.96 to 0.99), sensitivity of 0.91 (95% CI: 0.85 to 0.97), specificity of 0.99 (95% CI: 0.97 to 0.99), and AUC of 0.967. When evaluating and comparing the polyp detection ability of ALPHAON® with that of endoscopists with different levels of expertise (regarding years of endoscopic experience), it was found that ALPHAON® outperformed the experts in accuracy (0.97, 95% CI: 0.96 to 0.99), sensitivity (0.91, 95% CI: 0.85 to 0.97), and specificity (0.99, 95% CI: 0.97 to 0.99). After a washing-out period of over 2 weeks, the overall capability significantly improved for both experts and trainees with the assistance of ALPHAON®. The high performance of the CADe algorithm system in colon polyp detection during colonoscopy was verified. The sensitivity of ALPHAON® led to it outperforming the experts, and it demonstrated the ability to enhance the polyp detection ability of both experts and trainees, which suggests a significant possibility of ALPHAON® being able to provide endoscopic assistance.

Intra- and Postoperative Complications in 4565 vNOTES Hysterectomies: International Registry Cohort Study.

To present the rates of intra- and postoperative complications and conversions in a large cohort of unselected vNOTES hysterectomies, performed by surgeons with different levels of expertise. International register-based cohort study. Hysterectomies in the iNOTESs registry, 2015 to January 2024, performed by 201 surgeons from multiple countries. 4565 patients undergoing vNOTES hysterectomy. Descriptive data are presented in frequencies (n) and percent (%). Intra- and postoperative complications. Conversions. Intraoperative and postoperative complication rates were 3.2% (n = 144) and 2.5% (n = 115), respectively. Conversions occurred in 1.6% (n = 72), of which 10 (0.2%) to laparotomy, and 82% of the conversions occurred within the first 50 cases of the surgeon's learning curve. The most common intraoperative complication was cystotomy, occurring in 1.3%, and almost half were performed by inexperienced surgeons. Other intraoperative organ injuries occurred in 20 cases (0.44%). Postoperatively, the most common complications were haemorrhage (n = 28), vault complications (n = 26) including 11 infected vault hematomas, cystitis (n = 18) and non-specific infections (n = 14). The vNOTES hysterectomies were performed by 201 surgeons, of which 9.5% had performed more than 50 vNOTES cases, representing 70% of the registered cases in the registry. The remaining 30% of the hysterectomies mainly represent learning curve data from 90% of the included surgeons. The complication rate decreased with increasing surgical experience. The largest study population of vNOTES hysterectomies is presented, including both learning curve data and data from experienced surgeons, with acceptable rates of intra- and postoperative complications. No implication was found of vNOTES being inferior to other minimally invasive methods.

ChatGPT as a Source for Patient Information on Patellofemoral Surgery-A Comparative Study Amongst Laymen, Doctors, and Experts.

In November 2022, OpenAI launched ChatGPT for public use through a free online platform. ChatGPT is an artificial intelligence (AI) chatbot trained on a broad dataset encompassing a wide range of topics, including medical literature. The usability in the medical field and the quality of AI-generated responses are widely discussed and are the subject of current investigations. Patellofemoral pain is one of the most common conditions among young adults, often prompting patients to seek advice. This study examines the quality of ChatGPT as a source of information regarding patellofemoral conditions and surgery, hypothesizing that there will be differences in the evaluation of responses generated by ChatGPT between populations with different levels of expertise in patellofemoral disorders. A comparison was conducted between laymen, doctors (non-orthopedic), and experts in patellofemoral disorders based on a list of 12 questions. These questions were divided into descriptive and recommendatory categories, with each category further split into basic and advanced content. Questions were used to prompt ChatGPT in April 2024 using the ChatGPT 4.0 engine, and answers were evaluated using a custom tool inspired by the Ensuring Quality Information for Patients (EQIP) instrument. Evaluations were performed independently by laymen, non-orthopedic doctors, and experts, with the results statistically analyzed using a Mann-Whitney U Test. A p-value of less than 0.05 was considered statistically significant. The study included data from seventeen participants: four experts in patellofemoral disorders, seven non-orthopedic doctors, and six laymen. Experts rated the answers lower on average compared to non-experts. Significant differences were observed in the ratings of descriptive answers with increasing complexity. The average score for experts was 29.3 ± 5.8, whereas non-experts averaged 35.3 ± 5.7. For recommendatory answers, experts also gave lower ratings, particularly for more complex questions. ChatGPT provides good quality answers to questions concerning patellofemoral disorders, although questions with higher complexity were rated lower by patellofemoral experts compared to non-experts. This study emphasizes the potential of ChatGPT as a complementary tool for patient information on patellofemoral disorders, although the quality of the answers fluctuates with the complexity of the questions, which might not be recognized by non-experts. The lack of personalized recommendations and the problem of "AI hallucinations" remain a challenge. Human expertise and judgement, especially from trained healthcare experts, remain irreplaceable.

Abstract concepts and expertise: the case of institutional concepts

Recent views recognize that abstract concepts encompass a variety of exemplars, each relying on different dimensions, including not only sensorimotor but also inner, linguistic, and social experiences. How these dimensions characterize types of abstract concepts, and whether their weight varies across contexts and individuals remains an open question. We investigated the role of linguistic and social situations in the processing of institutional concepts, such as justice, by individuals with different levels of expertise. In a priming study, legal experts and non-experts were asked to respond to target words (go-trials) consisting of different kinds of abstract (institutional, theoretical) and concrete concepts (food, tools) and to ignore filler words (no-go trials). The verbal stimuli were primed by pictures depicting social-action, linguistic-social, linguistic-textual situations, and a control condition. As predicted, critical priming modulated performance on abstract concepts, likely due to their highly context-dependent meaning. Interestingly, we found that the processing of institutional concepts was selectively facilitated by social action prime, suggesting that this situational content may be integrated to support their representation. Crucially, the dialogic context, the linguistic social prime, affected more non-experts than law-experts, who tended to frame institutional concepts as shared idea for regulating social practices. Our results show that linguistic and social inputs become differently salient for institutional concepts representation depending on individual competence.

Exploring Chemists’ Understandings of the Nature of Science with their Levels of Expertise

Several Nature of Science (NOS) studies have been conducted on different categories of professionals and different age groups. Even though NOS is considered a structural facet of scientific literacy, not much research has been conducted on the NOS ideas of scientists and science professionals. This cross-sectional study investigated the nature of science (NOS) views of 40 participants who have or had exposure to chemistry research using a qualitative comparative analysis approach, in one chemistry department in a large Midwest R-1 university. The participants' chemistry research exposure varied from no research to 31 years of experience. Undergraduates, graduate students, postgraduate researchers, faculty, and scientists were recruited for this study to examine how their NOS understanding develops in relationship to their research exposure. These categories of participants demonstrated different and unique patterns of answers related to aspects of NOS. Science content knowledge and research exposure indeed enhanced science learners'/researchers’ NOS understanding, but NOS understanding is not developed in direct proportion to the amount of science content knowledge or research experience that an individual gained. It was observed that professionals with the same expertise level responded to NOS questions similarly, and professionals with different levels of expertise answered NOS questions in significantly different ways.

The Endocrine Feedback Loop Podcast: Past, Present, and Future.

In June 2024, the Endocrine Feedback Loop podcast recorded its fiftieth episode at the annual meeting of the Endocrine Society. Launched in May 2020, the podcast serves as a way for listeners to hear a critical analysis of recent and impactful research studies published in clinical journals of the Endocrine Society. The podcast follows methods proven effective in traditional journal clubs and adapts them to reach a wide audience with different levels of expertise in endocrinology. This Perspective outlines the history of the podcast, the process of producing a monthly episode, and the topics covered, as well as an assessment of the experience of producing the podcast and future plans for the Endocrine Feedback Loop.

A Classification for Syndesmosis Injuries Based on Soft Tissue and Bony Anatomy

Category: Trauma; Ankle Introduction/Purpose: Syndesmosis injuries result in potential separation to the intimate distal tibial and fibular connection/articulation and can result in meaningful ongoing disability and the development of early post-traumatic arthritis. The injury pattern includes ligament injuries, cartilage damage, and/or fractures. These injury patterns may predicate outcomes. The associated fractures and ligament injuries may also determine the nature of fixation and management required. While classification exists for posterior malleolar, fibular, and ankle fractures, no systematic approach exists for the classification and treatment of syndesmosis injuries. This study aims to describe a comprehensive classification system for syndesmosis injuries and determine its validity. Methods: Descriptive system: The ankle was divided into five zones based on bony and soft tissue anatomic locations: lateral (L), medial (M), posterior (P), anterior (A), and intraarticular (I). In each location no injury is type 0, soft tissue/avulsion injury type 1, and boney injury type 2. (Figure 1). Study group: Patients with operative syndesmosis injuries in a single institution between January 2002 and December 2012 were reviewed. Injury patterns were classified by 3 experts, 2 semi-experts, and 2 musculoskeletal radiologists based on orthogonal anteroposterior and lateral radiographs. Statistical analysis: A 103-patient cohort was deemed to be sufficient based on power analysis Weighted kappa statistics with quadratic weights was used for test retest analysis. For inter-rater reliability, a separate analysis was completed for experts, semi-experts, and radiologists. Intraclass correlation coefficients were obtained. Results: All injuries could be classified according to the proposed classification. The most frequent pattern was the type B221 (13%) and C221 (12%). The interobserver agreement among all observers was excellent for the medial (ICC of 0.847) and lateral aspect (ICC of 0.762), good for the posterior aspect (ICC of 0.615), and poor for the anterior aspect (ICC of 0.233) (Table 1). The classification showed good to excellent intraobserver agreement except for the anterior aspect, which had poor agreement for the semi-expert raters (Table2). Conclusion: The syndesmosis classification is a valid system to represent the complexity of injuries that may occur. Inter- and intraobserver agreement among raters with different levels of expertise in classifying injuries based only on X-ray was good to excellent, with the exception of the anterior aspect which may require additional imaging techniques for proper classification. The categorization of different injuries by their location allows for a better understanding of how injurie presents. It should lead to improved research outcomes and communication between professionals regarding injury patterns and potential treatments in the future, as not all syndesmosis injuries are the same.

An Efficient and Robust 3D Medical Image Classification Approach Based on 3D CNN, Time‐Distributed 2D CNN‐BLSTM Models, and mRMR Feature Selection

ABSTRACTThe advent of 3D medical imaging has been a turning point in the diagnosis of various diseases, as voxel information from adjacent slices helps radiologists better understand complex anatomical relationships. However, the interpretation of medical images by radiologists with different levels of expertise can vary and is also time‐consuming. In the last decades, artificial intelligence‐based computer‐aided systems have provided fast and more reliable diagnostic insights with great potential for various clinical purposes. This paper proposes a significant deep learning based 3D medical image diagnosis method. The method classifies MedMNIST3D, which consists of six 3D biomedical datasets obtained from CT, MRA, and electron microscopy modalities. The proposed method concatenates 3D image features extracted from three independent networks, a 3D CNN, and two time‐distributed ResNet BLSTM structures. The ultimate discriminative features are selected via the minimum redundancy maximum relevance (mRMR) feature selection method. Those features are then classified by a neural network model. Experiments adhere to the rules of the official splits and evaluation metrics of the MedMNIST3D datasets. The results reveal that the proposed approach outperforms similar studies in terms of accuracy and AUC.

Pulmonary embolism detection without intravenous contrast using electron density and Z-effective maps from dual-energy CT

Abstract Purpose This study aims to evaluate the feasibility of using electron density (ED) maps combined with Z-effective (Zeff) images obtained from unenhanced dual-layer dual-energy CT (dl-DECT) scans of the chest for the detection of pulmonary embolism (PE). Materials and methods A retrospective analysis was conducted on consecutive patients who underwent for contrast-enhanced chest CT (CECT) clinically suspected of PE or acute aortic syndrome. These scans were performed on a single dl-DECT scanner between October 2021 and November 2023. To distinguish emboli from circulating blood, color-coded maps were generated from the ED dataset superimposed on Zeff images, which were acquired from the unenhanced phase. Two radiologists with different levels of expertise independently assessed the presence of PE in the generated ED-Zeff maps, blinded to CECT results, which served as the reference standard. Diagnostic accuracy of ED-Zeff maps was assessed for each reader. Results The final study cohort comprised 150 patients, with 92 males (mean age: 68 ± 10 years, range: 47-93 years) and 58 females (mean age: 66 ± 15 years, range 38-89 years). ED-Zeff maps demonstrated high diagnostic performance, yielding accuracy, sensitivity, and specificity, respectively, of 86.67% (113/150, 95% CI, 80.16%-91.66%), 85% (17/20, 95% CI, 79.89%-92.19%), and 86.92% (113/130, 95% CI, 79.89%-92.19%). Ed-Zeff maps were able to identify PE in 85% of positive cases. Cohen’s kappa coefficient indicated excellent intra- and interobserver agreement (κ ≥ 0.9). Conclusion ED maps combined with Zeff images from unenhanced dl-DECT scans represent a feasible tool for detecting PE and may prove useful in evaluating patients with contraindications to iodinated contrast.

Reliability testing of sonographic techniques in detecting lateral displacement of patellar tendon

ObjectivesPatellofemoral pain syndrome (PFPS), a common knee condition, is characterized as presence of peripatellar pain during activities especially during loading of the lower extremity. Gonzalez-Suarez et al. (2021) developed a sonographic technique that assessed patellar tendon displacement within the trochlear groove. This study aims to determine the reliability of this sonographic technique. MethodsIntra-rater and inter-rater reliability study of three sonologist on the lateral displacement of the patellar tendon using ultrasound. Patellar tendon displacement was assessed using the three novel sonographic measurement techniques: 1) region of trochlear groove where the patellar tendon midpoint is located, 2) distance between deepest portion of the trochlear groove and patellar tendon midpoint and 3) percentage of lateral displacement of the patellar tendon. The measurements were done with the patients in supine and standing. ResultsTen participants with a mean age of 27.6 ± 1.56 years were included in the study. Pilot studies were done to improve the sonographic technique. Intraclass correlation coefficients yielded good to excellent inter-rater and excellent intra-rater reliability (>0.75) in the supine and standing positions except the inter-rater reliability of the region of the trochlear groove where patellar tendon midpoint is in supine position. ConclusionThe sonographic techniques for measurement of lateral patellar tendon displacement showed good to excellent intra-rater and inter-rater reliability between sonologists of different levels of expertise using a well-defined scanning protocol.

Validation and efficacy of the Varix Trainer model as a Training device for esophagogastroduodenoscopy.

Background and study aims The Varix Trainer model 1 (VTM1) was created for trainees to safely practice basic endoscope manipulation skills. The VTM1 was tested to see if it could distinguish levels of endoscope manipulation skills (construct validity) and whether training with it could improve these skills faster (content validity). Patients and methods We enrolled 23 novice endoscopists, 18 second-year trainees, and 13 expert endoscopists. They were asked to point with the endoscope tip to 20 numbers in the model as quickly as possible using torque, single-hand small/large wheel manipulation (SHSW), and retroflexion techniques. Their mean times (t20) were compared to determine if the model could distinguish different levels of expertise. Subsequently, 14 novices trained for eight short sessions, and the pre-training and post-training t20 were compared. Nine novice endoscopists received no training and were retested after 4 to 6 weeks (controls). Results Experts had faster t20 than second-year trainees, who were faster than novices, for all three techniques ( P < 0.001). After eight sessions, the mean t20 for novices improved from 112 to 66 seconds for torque, 144 to 72 seconds for SHSW, and 108 to 63 seconds for retroflexion, (all P < 0.001). Their t20 were equivalent to second-year trainees. Improvement in t20 was also seen with the control group, but total reduction was less than for the training group. Conclusions The VTM1 distinguished varying levels of expertise for all techniques, suggesting that it is a valid tool for assessing endoscope manipulation skill. A short curriculum improved novices' manipulation skills faster than traditional practice.

Feasibility and Educational Value of Clinical Cases Generated Using Large Language Models.

In medical education, case-based learning (CBL) is a fundamental method for training healthcare professionals across different levels of expertise. This approach hinges on using authentic or fabricated clinical cases to bridge the gap between theoretical knowledge and its practical application. It fosters active engagement and knowledge application among learners in healthcare domains. While creating effective cases demands substantial clinical understanding and time investment, the integration of Generative Artificial Intelligence (AI) presents a promising solution to this challenge. AI can efficiently analyze extensive medical data to generate diverse and realistic clinical scenarios, continuously updating case content based on emerging medical literature and guidelines. This study explores AI-generated cases' feasibility and educational value in continuing medical education, focusing on COVID-19 scenarios tailored for the MENA region. Results indicate the potential of AI-generated cases to foster engagement and critical thinking among learners, suggesting their suitability for different levels of education. This study highlights the advantages of integrating AI into CBL and emphasizes the need for future efforts to tackle obstacles and facilitate its successful adoption.

GPT-4o’s competency in answering the simulated written European Board of Interventional Radiology exam compared to a medical student and experts in Germany and its ability to generate exam items on interventional radiology: a descriptive study.

This study aimed to determine whether ChatGPT-4o, a generative artificial intelligence (AI) platform, was able to pass a simulated written European Board of Interventional Radiology (EBIR) exam and whether GPT-4o can be used to train medical students and interventional radiologists of different levels of expertise by generating exam items on interventional radiology. GPT-4o was asked to answer 370 simulated exam items of the Cardiovascular and Interventional Radiology Society of Europe (CIRSE) for EBIR preparation (CIRSE Prep). Subsequently, GPT-4o was requested to generate exam items on interventional radiology topics at levels of difficulty suitable for medical students and the EBIR exam. Those generated items were answered by 4 participants, including a medical student, a resident, a consultant, and an EBIR holder. The correctly answered items were counted. One investigator checked the answers and items generated by GPT-4o for correctness and relevance. This work was done from April to July 2024. GPT-4o correctly answered 248 of the 370 CIRSE Prep items (67.0%). For 50 CIRSE Prep items, the medical student answered 46.0%, the resident 42.0%, the consultant 50.0%, and the EBIR holder 74.0% correctly. All participants answered 82.0% to 92.0% of the 50 GPT-4o generated items at the student level correctly. For the 50 GPT-4o items at the EBIR level, the medical student answered 32.0%, the resident 44.0%, the consultant 48.0%, and the EBIR holder 66.0% correctly. All participants could pass the GPT-4o-generated items for the student level; while the EBIR holder could pass the GPT-4o-generated items for the EBIR level. Two items (0.3%) out of 150 generated by the GPT-4o were assessed as implausible. GPT-4o could pass the simulated written EBIR exam and create exam items of varying difficulty to train medical students and interventional radiologists.

Knowledge, attitudes and practices related to tooth morphology among dental students and dental technician students

Objectives: Students are introduced to the morphological features of primary and permanent human dentition in a dental anatomy course, which is a part of the basic core sciences program in the Department of Dentistry and Dental Prosthesis Technology. The purpose of this study was to compare dental students' (DSs’) and dental technician students' (DTSs’) knowledge, attitudes, and practices related to dental morphology. Materials and Methods: Using a 26-item self-administered questionnaire, the DSs and DTSs participated in a descriptive cross-sectional study. Three components made up the questionnaire: four items for demographic profile, ten items for dental morphology knowledge level, seven items for attitude toward tooth morphology, and five items for practice in tooth morphology. There were 274 student answers in all. The data were analyzed using an independent t test and one-way ANOVA. A p value of less than 0.05 was considered to indicate statistical significance for each test. Results: Between DSs and DTSs, there was a statistically significant difference in their knowledge and practices of tooth morphology (p&lt; .001). However, DTSs knowledge of dental morphology was significantly greater, and DSs practice scores were significantly greater. There was no significant difference in the attitude of tooth morphology between the two groups (p=0.379). Both groups had low levels of dental morphology knowledge, with an average of 3.86 correct answers for DTSs and an average of 3.16 correct answers for DSs. In the DSs group, first-year students had a greater knowledge level (4.22±1.63 correct answers) than did the other students. Conclusions: Knowing the variations in dental morphology knowledge, attitudes, and practices between a dentist and dental technician may help us take preventative measures. While preclinical dentistry students and dental technician students had different levels of expertise, both groups were less knowledgeable about tooth morphology. Both groups had similarly high attitudes toward tooth morphology.

Researching Behind the Badge: Challenges and Politics for Emerging Scholars in Police Studies

This study aims to find that the organizational structure and culture of the police have a significant impact particularly in terms of facilitating access, ensuring research independence, and developing long-term relationships. Police research has enabled the integration of grounded theoretical findings with the practical application of police strategies. However, scholars in police studies, especially those new to research, often face intricate dynamics and limitations, particularly concerning police culture and organizational obstacles. This paper seeks to examine the challenges and political barriers that new researchers have when studying the police. Employing an empirical qualitative approach, this study combines semi-structured interviews with six researchers who have different levels of expertise in police research. Additionally, auto-ethnographic reflections offer a thorough understanding of the subject matter. The research findings suggest that the organizational structure and culture of the police have a significant impact on research, particularly in terms of facilitating access, ensuring research independence, and developing long-lasting connections. We conclude by proposing that establishing an effective research collaboration with the police necessitates finding a balance between the interests of academics and the police institution. To achieve this, we advocate implementing mentorship programs that pair experienced researchers with junior researchers and create collaborative infrastructure to help new scholars.

Task-Allocation Decisions of Human-UAS Collaboration: Effects of Workload, Trust, and Self-confidence.

Uncrewed Aerial Systems (UAS) show promise in urban air transport, package delivery, and emergency services. UAS efficiency can be significantly improved by having multiple operators (m) managing a greater number of vehicles (N), or the m:N architecture of operation. The current study investigates how workload affects operators' task-allocation decision-making and the potential mediating effects of two crucial human factors, trust and self-confidence. In the context of a simulated UAS package-delivery task under the m:N architecture, two groups of participants with different levels of expertise in UAS operation will be recruited: UAS pilots and university students. Each participant will watch two sets of videos with different work-load manipulations and report their preferred task-allocation strategy for various subtasks. Measures of perceived workload, trust, and self-confidence will be conducted after each video session. Findings will inform optimizing task-allocation designs for UAS missions, considering operators' decision-making needs and expertise disparities.