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Objective: Exploring associations between antenatal detection of fetal growth restriction (FGR) and adverse outcome. Design: Retrospective, observational, register‐based study. Setting: Zealand, Denmark. Population or sample: Children born from 1 September 2012 to 31 August 2015. Methods: Diagnoses from birth until 1 January 2018 were retrieved from The National Patient Registry. Detection was defined as estimated fetal weight less than the 2.3rd centile. Cox regression was used to associate detection status with the hazard rate of adverse outcome, adjusted for fetal weight deviation, maternal age, ethnicity, body mass index and smoking. Main outcome measures: Adverse neonatal outcome, adverse neuropsychiatric outcome, respiratory disorders, endocrine disorders, gastrointestinal/urogenital disorders. Results: A total of 2425 FGR children were included. An association was found for gastrointestinal/urogenital disorders (hazard ratio [HR] 1.68, 95% CI 1.26–2.23, P < 0.001) and respiratory disorders (HR 1.22, 95% CI 1.02–1.46, P = 0.03) in detected versus undetected infants. For adverse neuropsychiatric outcome, HR was 1.32 (95% CI 1.00–1.75, P = 0.05). There was no evidence of an association between detection and adverse neonatal outcome (HR 1.00, 95% CI 0.62–1.61, P = 0.99) and endocrine disorders (HR 1.39, 95% CI 0.88–2.19, P = 0.16). Detected infants were smaller (median −28% versus −25%, P < 0.0001), more often born preterm (odds ratio [OR] 4.15, 3.12–5.52, P < 0.0001) and more often born after induction or caesarean section (OR 5.19, 95% CI 4.13–6.51, P < 0.0001). Stillbirth risk was increased in undetected FGR fetuses (OR 2.63, 95% CI 1.37–5.04, P = 0.004). Conclusions: We found an association between detection of FGR and risk of adverse childhood conditions, possibly caused by prematurity. Iatrogenic prematurity may be inevitable in stillbirth prevention, but is accompanied by a risk of long‐term childhood conditions. Antenatal detection of growth‐restricted fetuses is associated with adverse childhood outcomes but fewer intrauterine deaths. Antenatal detection of growth‐restricted fetuses is associated with adverse childhood outcomes but fewer intrauterine deaths. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to develop an automated skills assessment tool for surgical trainees using deep learning., Background: Optimal surgical performance in robot-assisted surgery (RAS) is essential for ensuring good surgical outcomes. This requires effective training of new surgeons, which currently relies on supervision and skill assessment by experienced surgeons. Artificial Intelligence (AI) presents an opportunity to augment existing human-based assessments., Methods: We used a network architecture consisting of a convolutional neural network combined with a long short-term memory (LSTM) layer to create two networks for the extraction and analysis of spatial and temporal features from video recordings of surgical procedures, facilitating action recognition and skill assessment., Results: 21 participants (16 novices and 5 experienced) performed 16 different intra-abdominal robot-assisted surgical procedures on porcine models. The action recognition network achieved an accuracy of 96.0% in identifying surgical actions. A GradCAM filter was used to enhance the model interpretability. The skill assessment network had an accuracy of 81.3% in classifying novices and experiences. Procedure plots were created to visualize the skill assessment., Conclusion: Our study demonstrated that AI can be used to automate surgical action recognition and skill assessment. The use of a porcine model enables effective data collection at different levels of surgical performance, which is normally not available in the clinical setting. Future studies need to test how well AI developed within a porcine setting can be used to detect errors and provide feedback and actionable skills assessment in the clinical setting., Competing Interests: Declarations. Conflict of interest: Dr. Nasseh Hashemi, Prof. Sten Rasmussen, Prof. Martin G. Tolsgaard, Dr. Mikkel L. Friis, Dr. Flemming Bjerrum, Dr. Lasse R. Østergaard, Dr. Morten Bo Søndergaard Svendsen, Eng. Mostaan Hashemi, and Eng. Matias Mose have no conflicts of interest to disclose. The study was supported by the Department of Clinical Medicine, Aalborg University, and Department of Urology, Aalborg University Hospital., (© 2025. The Author(s).)

Introduction: Decision-support tools for detecting physiological deterioration are widely used in clinical medicine but have been criticised for fostering a task-oriented culture and reducing the emphasis on clinical reasoning. Little is understood about what influences clinical decisions aided by decision-support tools, including professional standards, policies, and contextual factors. Therefore, we explored management reasoning employed by anaesthesiologists and PACU nurses in the post-anaesthesia care unit during the discharge of high-risk postoperative patients., Methods: A qualitative constructivist study, conducting 18 semi-structured with 6 anaesthesiologists and 12 nurses across three Danish teaching hospitals. We analysed data through thematic analysis, utilising Michael Lipsky's theory of "street-level bureaucracy" in combination with David A. Cook's Management Reasoning Framework as a sensitising concept., Results: Standards are frequently ambiguous, requiring interpretation and prioritisation. This allows for professional discretion by circumventing established policies, reducing task-oriented culture and enhancing the clinical reasoning processes. However, discretion in management reasoning depends on whether the clinician is inclined to uphold or adjust policies to maintain professional standards, influencing discharge decisions., Conclusion: While decision-support tools offer cognitive aid and help standardise patient trajectories, they also limit professional discretion in management reasoning and can potentially compromise care and treatment. This highlights the need for a balanced approach that considers both the benefits and limitations of these tools in clinical decision-making., (© 2025 Acta Anaesthesiologica Scandinavica Foundation.)

Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from European Union’s Horizon 2020 for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. GD owns equity interest in Artrya Ltd and provides consultancy services. JK-C receives research funding from GE, Genetech and is a consultant at Siloam Vision. GPK advises some AI startups such as Gleamer.AI, FLUIDDA BV, NanoX Vision and was the founder of Quantib BV. SEP is a consultant for Circle Cardiovascular Imaging, Calgary, Alberta, Canada. BG is employed by Kheiron Medical Technologies and HeartFlow. PL has/had grants/sponsored research agreements from Radiomics SA, Convert Pharmaceuticals SA and LivingMed Biotech srl. He received a presenter fee and/or reimbursement of travel costs/consultancy fee (in cash or in kind) from Astra Zeneca, BHV srl, and Roche. PL has/had minority shares in the companies Radiomics SA, Convert pharmaceuticals SA, Comunicare SA, LivingMed Biotech srl, and Bactam srl. PL is co-inventor of two issued patents with royalties on radiomics (PCT/NL2014/050248 and PCT/NL2014/050728), licensed to Radiomics SA; one issued patent on mtDNA (PCT/EP2014/059089), licensed to ptTheragnostic/DNAmito; one granted patent on LSRT (PCT/ P126537PC00, US patent No 12 102 842), licensed to Varian; one issued patent on Radiomic signature of hypoxia (US patent No 11 972 867), licensed to a commercial entity; one issued patent on Prodrugs (WO2019EP64112) without royalties; one non-issued, non-licensed patents on Deep Learning-Radiomics (N2024889) and three non-patented inventions (softwares) licensed to ptTheragnostic/DNAmito, Radiomics SA and Health Innovation Ventures. ARP serves as advisor for mGeneRX in exchange for equity. JM receives royalties from GE, research grants from Siemens and is unpaid consultant for Nuance. HCW owns minority shares in the company Radiomics SA. JWG serves on several radiology society AI committees. LR advises an AI startup Neurlamind. CPL is a shareholder and advisor to Bunker Hill Health, GalileoCDS, Sirona Medical, Adra, and Kheiron Medical. He serves as a board member of Bunker Hill Health and a shareholder of whiterabbit.ai. He has served as a paid consultant to Sixth Street and Gilmartin Capital. His institution has received grants or gifts from Bunker Hill Health, Carestream, CARPL, Clairity, GE Healthcare, Google Cloud, IBM, Kheiron, Lambda, Lunit, Microsoft, Philips, Siemens Healthineers, Stability.ai, Subtle Medical, VinBrain, Visiana, Whiterabbit.ai, the Lowenstein Foundation, and the Gordon and Betty Moore Foundation. GSC is a statistics editor for the BMJ and a National Institute for Health and Care Research (NIHR) Senior Investigator. The views expressed in this article are those of the author(s) and not necessarily those of the NIHR, or the Department of Health and Social Care. All other authors declare no competing interests.

Background: The Less Invasive Surfactant Administration Assessment Tool (LISA-AT) was developed to support operator training and competence assessment. This study aimed to gather validity evidence in the simulated setting to support using the LISA-AT scores., Methods: Validity evidence was gathered using the Messick framework. The lowest quartile (Q1) for the median of the experts' LISA-AT scores defined the minimum passing score., Results: Ten experts and 23 novices were enrolled in this study. Eight of the original 15 LISA-AT metrics effectively discriminated between novices and experts and demonstrated high test-retest reliability (Spearman's rho = 0.87), high internal consistency, and good inter-rater reliability (Cronbach's alpha = 0.88 and 0.82, respectively). The LISA-AT discriminated between novices' and experts' first two attempts with median [IQR] scores of 29 [26-32] vs 39 [39-40]). The minimum passing score was defined as 39/40 points, and the novices used a median [IQR] of 6 [5-7] attempts, ranging from 4 to 9 attempts, to reach this score. Compared with the expert group, the novices' laryngoscopy skills and time remained significantly different even after attaining the minimum passing score., Conclusion: We found strong validity evidence to support using the LISA-AT scores to train new LISA operators to the minimum passing score to ensure competence in the simulated setting., Impact: This study showed robust validity evidence for using the Less Invasive Surfactant Administration Assessment Tool (LISA-AT) to train LISA novices with a high test-retest reliability, high internal consistency, and good inter-rater reliability. Eight of the original 15 LISA-AT metrics effectively discriminated between novices and experts, with a minimum passing score of 39/40 points, corresponding to the lowest quartile (Q1) of the experts' performances. Novices typically needed six attempts in the simulated setting, ranging from four to nine. However, their laryngoscopy skills and duration remained significantly different from experts. Using the LISA-AT score can ensure competence in the simulated setting before advancing to supervised clinical procedures., Competing Interests: Competing interests: The authors declare no competing interests. Consent statement: This study did not include patients. All experts and novices gave electronic informed consent prior to study participation., (© 2025. The Author(s).)

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., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Disclosure: We used AI-assisted technology (GPT, Microsoft Word, Grammarly) to enhance the linguistic clarity of the paper. Our research group bears full responsibility for the content and ensures its accuracy in this paper., (© 2025. The Author(s).)

Background: Simulation-based training has gained distinction in cardiothoracic surgery as robotic-assisted cardiac procedures evolve. Despite the increasing use of wet lab simulators, the effectiveness of these training methods and skill acquisition rates remain poorly understood., Objectives: This study aimed to compare learning curves and assess the robotic cardiac surgical skill acquisition rate for cardiac and noncardiac surgeons who had no robotic experience in a wet lab simulation setting., Methods: In this prospective cohort study, participants practiced 3 robotic tasks in a porcine model: left atriotomy closure, internal thoracic artery harvesting and mitral annular suturing. Participants were novice robotic cardiac and noncardiac surgeons alongside experienced robotic cardiac surgeons who established performance benchmarks. Performance was evaluated using the time-based score and modified global evaluative assessment of robotic skills (mGEARS)., Results: The participants were 15 novice surgeons (7 cardiac; 8 noncardiac) and 4 experienced robotic surgeons. Most novices reached mastery in 52 (±22) min for atrial closure, 32 (±18) for internal thoracic artery harvesting and 34 (±12) for mitral stitches, with no significant differences between the cardiac and noncardiac surgeons. However, for mGEARS, noncardiac novices faced more challenges in internal thoracic artery harvesting. The Thurstone learning curve model indicated no significant difference in the learning rates between the groups., Conclusions: Wet lab simulation facilitates the rapid acquisition of robotic cardiac surgical skills to expert levels, irrespective of surgeons' experience in open cardiac surgery. These findings support the use of wet lab simulators for standardized, competency-based training in robotic cardiac surgery., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery.)

Background: Due to a multitude of factors, skin cancer incidence is increasing and challenges medical professionals in biopsy decision-making. While skin cancer may have a profound impact on the patient and be costly for society, there is little knowledge about the number and cost of benign skin lesions biopsied as collateral damage., Objectives: This study evaluates the number and costs of skin biopsies in Denmark over 15 years, focusing on benign and malignant skin lesions and melanomas across medical settings. It aims to determine the benign to malignant ratio (BMR) and number needed to biopsy (NNB) and estimate the direct cost of benign skin lesion biopsies in the Cancer Pathway from the perspective of a public healthcare system., Methods: The study included 4,481,207 biopsy specimens from January 2007 to June 2022 from the Danish Pathology Data Bank, of which 151,988 from the Cancer Pathway were included in the primary analysis of BMR. The national reimbursement rates for biopsies were used, alongside histopathological examination costs extracted from several pathology departments, for a Monte-Carlo simulation of a simple cost and sensitivity analysis., Results: The number of biopsies increased by 39.1% from 2007 to 2021. Overall BMR for malignancy was 4.1:1, and NNB for melanoma was 31.8, but biopsies performed on clinical suspicion of malignancy or melanoma had a BMR and NNB of 1.5:1 and 2.8, respectively. The cost of benign skin biopsies performed on suspicion of cancer or melanoma in 2021 was €6.6M, predominantly in hospitals., Conclusion: A healthcare system that employs filtering functions before biopsy of skin lesions can achieve some of the lowest BMR reported in the world, but with most benign skin lesion excisions due to suspicion of malignancy performed in the expensive hospital setting. Including clinical reason for biopsy in diagnostic accuracy studies using NNB is crucial., (© 2024 The Author(s). Journal of the European Academy of Dermatology and Venereology published by John Wiley & Sons Ltd on behalf of European Academy of Dermatology and Venereology.)

The objective of this study is to compare automated performance metrics (APM) and surgical gestures for technical skills assessment during simulated robot-assisted radical prostatectomy (RARP). Ten novices and six experienced RARP surgeons performed simulated RARPs on the RobotiX Mentor (Surgical Science, Sweden). Simulator APM were automatically recorded, and surgical videos were manually annotated with five types of surgical gestures. The consequences of the pass/fail levels, which were based on contrasting groups' methods, were compared for APM and surgical gestures. Intra-class correlation coefficient (ICC) analysis and a Bland-Altman plot were used to explore the correlation between APM and surgical gestures. Pass/fail levels for both APM and surgical gesture could fully distinguish between the skill levels of the surgeons with a specificity and sensitivity of 100%. The overall ICC (one-way, random) was 0.70 (95% CI: 0.34-0.88), showing moderate agreement between the methods. The Bland-Altman plot showed a high agreement between the two methods for assessing experienced surgeons but disagreed on the novice surgeons' skill level. APM and surgical gestures could both fully distinguish between novices and experienced surgeons in a simulated setting. Both methods of analyzing technical skills have their advantages and disadvantages and, as of now, those are only to a limited extent available in the clinical setting. The development of assessment methods in a simulated setting enables testing before implementing it in a clinical setting., (© 2024. The Author(s).)

Background and Aims: Intestinal ultrasound has become a crucial tool for assessing inflammation in patients with inflammatory bowel disease, prompting a surge in demand for trained sonographers. While educational programs exist, the length of training needed to reach proficiency in correctly classifying inflammation remains unclear. Our study addresses this gap partly by exploring the learning curves associated with the deliberate practice of sonographic disease assessment, focusing on the key disease activity parameters of bowel wall thickness, bowel wall stratification, color Doppler signal, and inflammatory fat., Methods: Twenty-one novices and six certified intestinal ultrasound practitioners engaged in an 80-case deliberate practice online training program. A panel of three experts independently graded ultrasound images representing various degrees of disease activity and agreed upon a consensus score. We used statistical analyses, including mixed-effects regression models, to evaluate learning trajectories. Pass/fail thresholds distinguishing novices from certified practitioners were determined through contrasting-groups analyses., Results: Novices showed significant improvement in interpreting bowel wall thickness, surpassing the pass/fail threshold, and reached mastery level by case 80. For color Doppler signal and inflammatory fat, novices surpassed the pass/fail threshold but did not achieve mastery. Novices did not improve in assessing bowel wall stratification., Conclusions: We found considerable individual and group-level differences in learning curves supporting the concept of competency-based training for assessing bowel wall thickness, color Doppler signal and inflammatory fat. However, despite practice over 80 cases, novices did not improve in their interpretation of bowel wall stratification, suggesting that a different approach is needed for this parameter., (© The Author(s) 2024. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: A significant factor of clinicians' learning is based on their ability to effectively transfer acquired knowledge, skills, and attitudes from specialty-specific clinical courses to their working environment., Material and Method: We conducted semi-structured interviews with 20 anaesthesiologist trainees (i.e. residents) in four group and five individual interviews using SRL principles as sensitizing concepts. Data were collected and analyzed iteratively using thematic analysis., Results: Advanced trainees are highly motivated to explore what they have learned in specialty-specific courses, but they often face several barriers in implementing their learning in the workplace environment. Four themes emerged from the interview data: 'Be ready to learn', "Take the 'take-home-messages' home', "Be ready to create your own opportunities', and "Face it, it's not entirely up to you'. Understanding the challenges regarding transferring knowledge from courses to the working environment is an important lesson for assisting trainees set their learning goals, monitor their progress, and re-evaluate their SRL processes., Conclusion: Even for advanced trainees, successfully transferring knowledge from specialty-specific courses often requires adequate commitment and support. Medical supervisors and other relevant stakeholders must be aware of their shared responsibility for creating individual environments that support opportunities for trainees to self-regulate their learning.

Objective: Although remarkable strides have been made in fetal medicine and the prenatal diagnosis of congenital heart disease, around 60% of newborns with isolated coarctation of the aorta (CoA) are not identified prior to birth. The prenatal detection of CoA has been shown to have a notable impact on survival rates of affected infants. To this end, implementation of artificial intelligence (AI) in fetal ultrasound may represent a groundbreaking advance. We aimed to investigate whether the use of automated cardiac biometric measurements with AI during the 18-22-week anomaly scan would enhance the identification of fetuses that are at risk of developing CoA., Methods: We developed an AI model capable of identifying standard cardiac planes and conducting automated cardiac biometric measurements. Our data consisted of pregnancy ultrasound image and outcome data spanning from 2008 to 2018 and collected from four distinct regions in Denmark. Cases with a postnatal diagnosis of CoA were paired with healthy controls in a ratio of 1:100 and matched for gestational age within 2 days. Cardiac biometrics obtained from the four-chamber and three-vessel views were included in a logistic regression-based prediction model. To assess its predictive capabilities, we assessed sensitivity and specificity on receiver-operating-characteristics (ROC) curves., Results: At the 18-22-week scan, the right ventricle (RV) area and length, left ventricle (LV) diameter and the ratios of RV/LV areas and main pulmonary artery/ascending aorta diameters showed significant differences, with Z-scores above 0.7, when comparing subjects with a postnatal diagnosis of CoA (n = 73) and healthy controls (n = 7300). Using logistic regression and backward feature selection, our prediction model had an area under the ROC curve of 0.96 and a specificity of 88.9% at a sensitivity of 90.4%., Conclusions: The integration of AI technology with automated cardiac biometric measurements obtained during the 18-22-week anomaly scan has the potential to enhance substantially the performance of screening for fetal CoA and subsequently the detection rate of CoA. Future research should clarify how AI technology can be used to aid in the screening and detection of congenital heart anomalies to improve neonatal outcomes. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology., (© 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.)

The use of the p-value in quantitative research, particularly its threshold of "P < 0.05" for determining "statistical significance," has long been a cornerstone of statistical analysis in research. However, this standard has been increasingly scrutinized for its potential to mislead findings, especially when the practical significance, the number of comparisons, or the suitability of statistical tests are not properly considered. In response to controversy around use of p-values, the American Statistical Association published a statement in 2016 that challenged the research community to abandon the term "statistically significant". This stance has been echoed by leading scientific journals to urge a significant reduction or complete elimination in the reliance on p-values when reporting results. To provide guidance to researchers in health professions education, this paper provides a succinct overview of the ongoing debate regarding the use of p-values and the definition of p-values. It reflects on the controversy by highlighting the common pitfalls associated with p-value interpretation and usage, such as misinterpretation, overemphasis, and false dichotomization between "significant" and "non-significant" results. This paper also outlines specific recommendations for the effective use of p-values in statistical reporting including the importance of reporting effect sizes, confidence intervals, the null hypothesis, and conducting sensitivity analyses for appropriate interpretation. These considerations aim to guide researchers toward a more nuanced and informative use of p-values., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2024 The Author(s).)

Purpose: This study aimed to assess the validity of simulation-based assessment of ultrasound skills for thyroid ultrasound., Methods: The study collected validity evidence for simulation-based ultrasound assessment of thyroid ultrasound skills. Experts (n = 8) and novices (n = 21) completed a test containing two tasks and four cases on a virtual reality ultrasound simulator (U/S Mentor's Neck Ultrasound Module). Validity evidence was collected and structured according to Messick's validity framework. The assessments being evaluated included built-in simulator metrics and expert-based evaluations using the Objective Structured Assessment of Ultrasound Skills (OSAUS) scale., Results: Out of 64 built-in simulator metrics, 9 (14.1%) exhibited validity evidence. The internal consistency of these metrics was strong (Cronbach's α = 0.805) with high test-retest reliability (intraclass correlation coefficient = 0.911). Novices achieved an average score of 41.9% (SD = 24.3) of the maximum, contrasting with experts at 81.9% (SD = 16.7). Time comparisons indicated minor differences between experts (median: 359 s) and novices (median: 376.5 s). All OSAUS items differed significantly between the two groups. The correlation between correctly entered clinical findings and the OSAUS scores was 0.748 (p < 0.001). The correlation between correctly entered clinical findings and the metric scores was 0.801 (p < 0.001)., Conclusion: While simulation-based training is promising, only 14% of built-in simulator metrics could discriminate between novices and ultrasound experts. Already-established competency frameworks such as OSAUS provided strong validity evidence for the assessment of otorhinolaryngology ultrasound competence., (© 2024. The Author(s).)

Changes in digital technology, increasing volume of data collection, and advances in methods have the potential to unleash the value of big data generated through the education of health professionals. Coupled with this potential are legitimate concerns about how data can be used or misused in ways that limit autonomy, equity, or harm stakeholders. This consensus statement is intended to address these issues by foregrounding the ethical imperatives for engaging with big data as well as the potential risks and challenges. Recognizing the wide and ever evolving scope of big data scholarship, we focus on foundational issues for framing and engaging in research. We ground our recommendations in the context of big data created through data sharing across and within the stages of the continuum of the education and training of health professionals. Ultimately, the goal of this statement is to support a culture of trust and quality for big data research to deliver on its promises for health professions education (HPE) and the health of society. Based on expert consensus and review of the literature, we report 19 recommendations in (1) framing scholarship and research through research, (2) considering unique ethical practices, (3) governance of data sharing collaborations that engage stakeholders, (4) data sharing processes best practices, (5) the importance of knowledge translation, and (6) advancing the quality of scholarship through multidisciplinary collaboration. The recommendations were modified and refined based on feedback from the 2022 Ottawa Conference attendees and subsequent public engagement. Adoption of these recommendations can help HPE scholars share data ethically and engage in high impact big data scholarship, which in turn can help the field meet the ultimate goal: high-quality education that leads to high-quality healthcare.

This study aimed to develop a deep learning model to assess the quality of fetal echocardiography and to perform prospective clinical validation. The model was trained on data from the 18-22-week anomaly scan conducted in seven hospitals from 2008 to 2018. Prospective validation involved 100 patients from two hospitals. A total of 5363 images from 2551 pregnancies were used for training and validation. The model's segmentation accuracy depended on image quality measured by a quality score (QS). It achieved an overall average accuracy of 0.91 (SD 0.09) across the test set, with images having above-average QS scoring 0.97 (SD 0.03). During prospective validation of 192 images, clinicians rated 44.8% (SD 9.8) of images as equal in quality, 18.69% (SD 5.7) favoring auto-captured images and 36.51% (SD 9.0) preferring manually captured ones. Images with above average QS showed better agreement on segmentations (p < 0.001) and QS (p < 0.001) with fetal medicine experts. Auto-capture saved additional planes beyond protocol requirements, resulting in more comprehensive echocardiographies. Low QS had adverse effect on both model performance and clinician's agreement with model feedback. The findings highlight the importance of developing and evaluating AI models based on 'noisy' real-life data rather than pursuing the highest accuracy possible with retrospective academic-grade data., (© 2024. The Author(s).)

To collect validity evidence for the assessment of surgical competence through the classification of general surgical gestures for a simulated robot-assisted radical prostatectomy (RARP). We used 165 video recordings of novice and experienced RARP surgeons performing three parts of the RARP procedure on the RobotiX Mentor. We annotated the surgical tasks with different surgical gestures: dissection, hemostatic control, application of clips, needle handling, and suturing. The gestures were analyzed using idle time (periods with minimal instrument movements) and active time (whenever a surgical gesture was annotated). The distribution of surgical gestures was described using a one-dimensional heat map, snail tracks. All surgeons had a similar percentage of idle time but novices had longer phases of idle time (mean time: 21 vs. 15 s, p < 0.001). Novices used a higher total number of surgical gestures (number of phases: 45 vs. 35, p < 0.001) and each phase was longer compared with those of the experienced surgeons (mean time: 10 vs. 8 s, p < 0.001). There was a different pattern of gestures between novices and experienced surgeons as seen by a different distribution of the phases. General surgical gestures can be used to assess surgical competence in simulated RARP and can be displayed as a visual tool to show how performance is improving. The established pass/fail level may be used to ensure the competence of the residents before proceeding with supervised real-life surgery. The next step is to investigate if the developed tool can optimize automated feedback during simulator training., (© 2024. The Author(s).)

Background: Ultrasound is a safe and effective diagnostic tool used within several specialties. However, the quality of ultrasound scans relies on sufficiently skilled clinician operators. The aim of this study was to explore the validity of automated assessments of upper abdominal ultrasound skills using an ultrasound simulator., Methods: Twenty five novices and five experts were recruited, all of whom completed an assessment program for the evaluation of upper abdominal ultrasound skills on a virtual reality simulator. The program included five modules that assessed different organ systems using automated simulator metrics. We used Messick's framework to explore the validity evidence of these simulator metrics to determine the contents of a final simulator test. We used the contrasting groups method to establish a pass/fail level for the final simulator test., Results: Thirty seven out of 60 metrics were able to discriminate between novices and experts (p < 0.05). The median simulator score of the final simulator test including the metrics with validity evidence was 26.68% (range: 8.1-40.5%) for novices and 85.1% (range: 56.8-91.9%) for experts. The internal structure was assessed by Cronbach alpha (0.93) and intraclass correlation coefficient (0.89). The pass/fail level was determined to be 50.9%. This pass/fail criterion found no passing novices or failing experts., Conclusions: This study collected validity evidence for simulation-based assessment of upper abdominal ultrasound examinations, which is the first step toward competency-based training. Future studies may examine how competency-based training in the simulated setting translates into improvements in clinical performances., (© 2024. The Author(s).)

Introduction: Simulation-based training (SBT) aids healthcare providers in acquiring the technical skills necessary to improve patient outcomes and safety. However, since SBT may require significant resources, training all skills to a comparable extent is impractical. Hence, a strategic prioritization of technical skills is necessary. While the European Training Requirements in Neonatology provide guidance on necessary skills, they lack prioritization. We aimed to identify and prioritize technical skills for a SBT curriculum in neonatology., Methods: A three-round modified Delphi process of expert neonatologists and neonatal trainees was performed. In round one, the participants listed all the technical skills newly trained neonatologists should master. The content analysis excluded duplicates and non-technical skills. In round two, the Copenhagen Academy for Medical Education and Simulation Needs Assessment Formula (CAMES-NAF) was used to preliminarily prioritize the technical skills according to frequency, importance of competency, SBT impact on patient safety, and feasibility for SBT. In round three, the participants further refined and reprioritized the technical skills. Items achieving consensus (agreement of ≥75%) were included., Results: We included 168 participants from 10 European countries. The response rates in rounds two and three were 80% (135/168) and 87% (117/135), respectively. In round one, the participants suggested 1964 different items. Content analysis revealed 81 unique technical skills prioritized in round two. In round three, 39 technical skills achieved consensus and were included., Conclusion: We reached a European consensus on a prioritized list of 39 technical skills to be included in a SBT curriculum in neonatology., (© 2024 S. Karger AG, Basel.)

Background: Artificial intelligence (AI) is becoming increasingly used in medical education, but our understanding of the validity of AI-based assessments (AIBA) as compared with traditional clinical expert-based assessments (EBA) is limited. In this study, the authors aimed to compare and contrast the validity evidence for the assessment of a complex clinical skill based on scores generated from an AI and trained clinical experts, respectively., Methods: The study was conducted between September 2020 to October 2022. The authors used Kane's validity framework to prioritise and organise their evidence according to the four inferences: scoring, generalisation, extrapolation and implications. The context of the study was chorionic villus sampling performed within the simulated setting. AIBA and EBA were used to evaluate performances of experts, intermediates and novice based on video recordings. The clinical experts used a scoring instrument developed in a previous international consensus study. The AI used convolutional neural networks for capturing features on video recordings, motion tracking and eye movements to arrive at a final composite score., Results: A total of 45 individuals participated in the study (22 novices, 12 intermediates and 11 experts). The authors demonstrated validity evidence for scoring, generalisation, extrapolation and implications for both EBA and AIBA. The plausibility of assumptions related to scoring, evidence of reproducibility and relation to different training levels was examined. Issues relating to construct underrepresentation, lack of explainability, and threats to robustness were identified as potential weak links in the AIBA validity argument compared with the EBA validity argument., Conclusion: There were weak links in the use of AIBA compared with EBA, mainly in their representation of the underlying construct but also regarding their explainability and ability to transfer to other datasets. However, combining AI and clinical expert-based assessments may offer complementary benefits, which is a promising subject for future research., (© 2023 The Authors. Medical Education published by Association for the Study of Medical Education and John Wiley & Sons Ltd.)

Background: Ultrasound is an essential diagnostic examination used in several medical specialties. However, the quality of ultrasound examinations is dependent on mastery of certain skills, which may be difficult and costly to attain in the clinical setting. This study aimed to explore mastery learning for trainees practicing general abdominal ultrasound using a virtual reality simulator and to evaluate the associated cost per student achieving the mastery learning level., Methods: Trainees were instructed to train on a virtual reality ultrasound simulator until the attainment of a mastery learning level was established in a previous study. Automated simulator scores were used to track performances during each round of training, and these scores were recorded to determine learning curves. Finally, the costs of the training were evaluated using a micro-costing procedure., Results: Twenty-one out of the 24 trainees managed to attain the predefined mastery level two times consecutively. The trainees completed their training with a median of 2h38min (range: 1h20min-4h30min) using a median of 7 attempts (range: 3-11 attempts) at the simulator test. The cost of training one trainee to the mastery level was estimated to be USD 638., Conclusion: Complete trainees can obtain mastery learning levels in general abdominal ultrasound examinations within 3 hours of training in the simulated setting and at an average cost of USD 638 per trainee. Future studies are needed to explore how the cost of simulation-based training is best balanced against the costs of clinical training., (© 2023. The Author(s).)

Objective: This study aimed to investigate the validity of simulation-based assessment of robotic-assisted cardiac surgery skills using a wet lab model, focusing on the use of a time-based score (TBS) and modified Global Evaluative Assessment of Robotic Skills (mGEARS) score., Methods: We tested 3 wet lab tasks (atrial closure, mitral annular stitches, and internal thoracic artery [ITA] dissection) with both experienced robotic cardiac surgeons and novices from multiple European centers. The tasks were assessed using 2 tools: TBS and mGEARS score. Reliability, internal consistency, and the ability to discriminate between different levels of competence were evaluated., Results: The results demonstrated a high internal consistency for all 3 tasks using mGEARS assessment tool. The mGEARS score and TBS could reliably discriminate between different levels of competence for the atrial closure and mitral stitches tasks but not for the ITA harvesting task. A generalizability study also revealed that it was feasible to assess competency of the atrial closure and mitral stitches tasks using mGEARS but not the ITA dissection task. Pass/fail scores were established for each task using both TBS and mGEARS assessment tools., Conclusions: The study provides sufficient evidence for using TBS and mGEARS scores in evaluating robotic-assisted cardiac surgery skills in wet lab settings for intracardiac tasks. Combining both assessment tools enhances the evaluation of proficiency in robotic cardiac surgery, paving the way for standardized, evidence-based preclinical training and credentialing., Clinical Trial Registry Number: NCT05043064., Competing Interests: The authors reported no conflicts of interest. The Journal policy requires editors and reviewers to disclose conflicts of interest and to decline handling or reviewing manuscripts for which they may have a conflict of interest. The editors and reviewers of this article have no conflicts of interest., (© 2023 The Author(s).)

Background: Testing is critical for detecting SARS-CoV-2 infection, but the best sampling method remains unclear., Objectives: To determine whether nasopharyngeal swab (NPS), oropharyngeal swab (OPS) or saliva specimen collection has the highest detection rate for SARS-CoV-2 molecular testing., Methods: We conducted a randomised clinical trial at two COVID-19 outpatient test centres where NPS, OPS and saliva specimens were collected by healthcare workers in different orders for reverse transcriptase PCR testing. The SARS-CoV-2 detection rate was calculated as the number positive by a specific sampling method divided by the number in which any of the three sampling methods was positive. As secondary outcomes, test-related discomfort was measured with an 11-point numeric scale and cost-effectiveness was calculated., Results: Among 23 102 adults completing the trial, 381 (1.65%) were SARS-CoV-2 positive. The SARS-CoV-2 detection rate was higher for OPSs, 78.7% (95% CI 74.3 to 82.7), compared with NPSs, 72.7% (95% CI 67.9 to 77.1) (p=0.049) and compared with saliva sampling, 61.9% (95% CI 56.9 to 66.8) (p<0.001). The discomfort score was highest for NPSs, at 5.76 (SD, 2.52), followed by OPSs, at 3.16 (SD 3.16) and saliva samples, at 1.03 (SD 18.8), p<0.001 between all measurements. Saliva specimens were associated with the lowest cost, and the incremental costs per detected SARS-CoV-2 infection for NPSs and OPSs were US$3258 and US$1832, respectively., Conclusions: OPSs were associated with higher SARS-CoV-2 detection and lower test-related discomfort than NPSs for SARS-CoV-2 testing. Saliva sampling had the lowest SARS-CoV-2 detection but was the least costly strategy for mass testing., Trial Registration Number: NCT04715607., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Training and assessment of operator competence for the less invasive surfactant administration (LISA) procedure vary. This study aimed to obtain international expert consensus on LISA training (LISA curriculum (LISA-CUR)) and assessment (LISA assessment tool (LISA-AT))., Methods: From February to July 2022, an international three-round Delphi process gathered opinions from LISA experts (researchers, curriculum developers, and clinical educators) on a list of items to be included in a LISA-CUR and LISA-AT (Round 1). The experts rated the importance of each item (Round 2). Items supported by more than 80% consensus were included. All experts were asked to approve or reject the final LISA-CUR and LISA-AT (Round 3)., Results: A total of 153 experts from 14 countries participated in Round 1, and the response rate for Rounds 2 and 3 was >80%. Round 1 identified 44 items for LISA-CUR and 22 for LISA-AT. Round 2 excluded 15 items for the LISA-CUR and 7 items for the LISA-AT. Round 3 resulted in a strong consensus (99-100%) for the final 29 items for the LISA-CUR and 15 items for the LISA-AT., Conclusions: This Delphi process established an international consensus on a training curriculum and content evidence for the assessment of LISA competence., Impact: This international consensus-based expert statement provides content on a curriculum for the less invasive surfactant administration procedure (LISA-CUR) that may be partnered with existing evidence-based strategies to optimize and standardize LISA training in the future. This international consensus-based expert statement also provides content on an assessment tool for the LISA procedure (LISA-AT) that can help to evaluate competence in LISA operators. The proposed LISA-AT enables standardized, continuous feedback and assessment until achieving proficiency., (© 2023. The Author(s).)

Background: Skin cancer diagnostics is challenging, and mastery requires extended periods of dedicated practice., Objective: The aim of the study was to determine if self-paced pattern recognition training in skin cancer diagnostics with clinical and dermoscopic images of skin lesions using a large-scale interactive image repository (LIIR) with patient cases improves primary care physicians' (PCPs') diagnostic skills and confidence., Methods: A total of 115 PCPs were randomized (allocation ratio 3:1) to receive or not receive self-paced pattern recognition training in skin cancer diagnostics using an LIIR with patient cases through a quiz-based smartphone app during an 8-day period. The participants' ability to diagnose skin cancer was evaluated using a 12-item multiple-choice questionnaire prior to and 8 days after the educational intervention period. Their thoughts on the use of dermoscopy were assessed using a study-specific questionnaire. A learning curve was calculated through the analysis of data from the mobile app., Results: On average, participants in the intervention group spent 2 hours 26 minutes quizzing digital patient cases and 41 minutes reading the educational material. They had an average preintervention multiple choice questionnaire score of 52.0% of correct answers, which increased to 66.4% on the postintervention test; a statistically significant improvement of 14.3 percentage points (P<.001; 95% CI 9.8-18.9) with intention-to-treat analysis. Analysis of participants who received the intervention as per protocol (500 patient cases in 8 days) showed an average increase of 16.7 percentage points (P<.001; 95% CI 11.3-22.0) from 53.9% to 70.5%. Their overall ability to correctly recognize malignant lesions in the LIIR patient cases improved over the intervention period by 6.6 percentage points from 67.1% (95% CI 65.2-69.3) to 73.7% (95% CI 72.5-75.0) and their ability to set the correct diagnosis improved by 10.5 percentage points from 42.5% (95% CI 40.2%-44.8%) to 53.0% (95% CI 51.3-54.9). The diagnostic confidence of participants in the intervention group increased on a scale from 1 to 4 by 32.9% from 1.6 to 2.1 (P<.001). Participants in the control group did not increase their postintervention score or their diagnostic confidence during the same period., Conclusions: Self-paced pattern recognition training in skin cancer diagnostics through the use of a digital LIIR with patient cases delivered by a quiz-based mobile app improves the diagnostic accuracy of PCPs., Trial Registration: ClinicalTrials.gov NCT05661370; https://classic.clinicaltrials.gov/ct2/show/NCT05661370., (©Gustav Gede Nervil, Niels Kvorning Ternov, Tine Vestergaard, Henrik Sølvsten, Annette Hougaard Chakera, Martin Grønnebæk Tolsgaard, Lisbet Rosenkrantz Hölmich. Originally published in JMIR Dermatology (http://derma.jmir.org), 09.08.2023.)

Background: The increasing use of robot-assisted surgery (RAS) has led to the need for new methods of assessing whether new surgeons are qualified to perform RAS, without the resource-demanding process of having expert surgeons do the assessment. Computer-based automation and artificial intelligence (AI) are seen as promising alternatives to expert-based surgical assessment. However, no standard protocols or methods for preparing data and implementing AI are available for clinicians. This may be among the reasons for the impediment to the use of AI in the clinical setting., Method: We tested our method on porcine models with both the da Vinci Si and the da Vinci Xi. We sought to capture raw video data from the surgical robots and 3D movement data from the surgeons and prepared the data for the use in AI by a structured guide to acquire and prepare video data using the following steps: 'Capturing image data from the surgical robot', 'Extracting event data', 'Capturing movement data of the surgeon', 'Annotation of image data'., Results: 15 participant (11 novices and 4 experienced) performed 10 different intraabdominal RAS procedures. Using this method we captured 188 videos (94 from the surgical robot, and 94 corresponding movement videos of the surgeons' arms and hands). Event data, movement data, and labels were extracted from the raw material and prepared for use in AI., Conclusion: With our described methods, we could collect, prepare, and annotate images, events, and motion data from surgical robotic systems in preparation for its use in AI., (© 2023. The Author(s).)

The use of Artificial Intelligence (AI) in medical education has the potential to facilitate complicated tasks and improve efficiency. For example, AI could help automate assessment of written responses, or provide feedback on medical image interpretations with excellent reliability. While applications of AI in learning, instruction, and assessment are growing, further exploration is still required. There exist few conceptual or methodological guides for medical educators wishing to evaluate or engage in AI research. In this guide, we aim to: 1) describe practical considerations involved in reading and conducting studies in medical education using AI, 2) define basic terminology and 3) identify which medical education problems and data are ideally-suited for using AI.

The use of medical simulators for training technical and diagnostic skills has rapidly increased over the past decade. Yet, most available medical simulators have not been developed based on a structured evaluation of their intended uses but rather out of expected commercial value. Moreover, educators often struggle to access simulators because of cost or because no simulators have been developed for a particular procedure. In this report, we introduce "the V-model" as a conceptual framework to illustrate how simulator development can be guided by the intended uses in an iterative fashion. Applying a needs-based conceptual framework when developing simulators is important to increase the accessibility and sustainability of simulation-based medical education. It will minimize the developmental barriers and costs, while at the same time improving educational outcomes. Two new simulators for invasive ultrasound-guided procedures are used as examples, the chorionic villus sampling model and the ultrasound-guided aspiration trainer. Our conceptual framework and the use cases can serve as a template for future simulator development and documentation hereof., (© 2023. The Author(s).)

Most artificial intelligence (AI) research and innovations have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa, the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for the diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with low resources, i.e. with limited access to high-end ultrasound equipment and ultrasound data. This work investigates for the first time different strategies to reduce the domain-shift effect arising from a fetal plane classification model trained on one clinical centre with high-resource settings and transferred to a new centre with low-resource settings. To that end, a classifier trained with 1792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach for domain adaptation can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to [Formula: see text] and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for the usability of AI in countries with fewer resources and, consequently, in higher need of clinical support., (© 2023. The Author(s).)

The placenta is crucial to fetal well-being and it plays a significant role in the pathogenesis of hypertensive pregnancy disorders. Moreover, a timely diagnosis of placenta previa may save lives. Ultrasound is the primary imaging modality in pregnancy, but high-quality imaging depends on the access to equipment and staff, which is not possible in all settings. Convolutional neural networks may help standardize the acquisition of images for fetal diagnostics. Our aim was to develop a deep learning based model for classification and segmentation of the placenta in ultrasound images. We trained a model based on manual annotations of 7,500 ultrasound images to identify and segment the placenta. The model's performance was compared to annotations made by 25 clinicians (experts, trainees, midwives). The overall image classification accuracy was 81%. The average intersection over union score (IoU) reached 0.78. The model's accuracy was lower than experts' and trainees', but it outperformed all clinicians at delineating the placenta, IoU = 0.75 vs 0.69, 0.66, 0.59. The model was cross validated on 100 2nd trimester images from Barcelona, yielding an accuracy of 76%, IoU 0.68. In conclusion, we developed a model for automatic classification and segmentation of the placenta with consistent performance across different patient populations. It may be used for automated detection of placenta previa and enable future deep learning research in placental dysfunction., (© 2023. The Author(s).)

Introduction: In image interpretation education, many educators have shifted away from traditional methods that involve passive instruction and fragmented learning to interactive ones that promote active engagement and integrated knowledge. By training pattern recognition skills in an effective manner, these interactive approaches provide a promising direction for dermoscopy education., Objectives: A narrative review of the literature was performed to probe emerging directions in medical image interpretation education that may support dermoscopy education. This article represents the second of a two-part review series., Methods: To promote innovation in dermoscopy education, the International Skin Imaging Collaborative (ISIC) assembled an Education Working Group that comprises international dermoscopy experts and educational scientists. Based on a preliminary literature review and their experiences as educators, the group developed and refined a list of innovative approaches through multiple rounds of discussion and feedback. For each approach, literature searches were performed for relevant articles., Results: Through a consensus-based approach, the group identified a number of theory-based approaches, as discussed in the first part of this series. The group also acknowledged the role of motivation, metacognition, and early failures in optimizing the learning process. Other promising teaching tools included gamification, social media, and perceptual and adaptive learning modules (PALMs)., Conclusions: Over the years, many dermoscopy educators may have intuitively adopted these instructional strategies in response to learner feedback, personal observations, and changes in the learning environment. For dermoscopy training, PALMs may be especially valuable in that they provide immediate feedback and adapt the training schedule to the individual's performance., Competing Interests: Competing interests: None., (©2022 Tran et al.)

Introduction: Efficient interpretation of dermoscopic images relies on pattern recognition, and the development of expert-level proficiency typically requires extensive training and years of practice. While traditional methods of transferring knowledge have proven effective, technological advances may significantly improve upon these strategies and better equip dermoscopy learners with the pattern recognition skills required for real-world practice., Objectives: A narrative review of the literature was performed to explore emerging directions in medical image interpretation education that may enhance dermoscopy education. This article represents the first of a two-part review series on this topic., Methods: To promote innovation in dermoscopy education, the International Skin Imaging Collaborative (ISIC) assembled a 12-member Education Working Group that comprises international dermoscopy experts and educational scientists. Based on a preliminary literature review and their experiences as educators, the group developed and refined a list of innovative approaches through multiple rounds of discussion and feedback. For each approach, literature searches were performed for relevant articles., Results: Through a consensus-based approach, the group identified a number of emerging directions in image interpretation education. The following theory-based approaches will be discussed in this first part: whole-task learning, microlearning, perceptual learning, and adaptive learning., Conclusions: Compared to traditional methods, these theory-based approaches may enhance dermoscopy education by making learning more engaging and interactive and reducing the amount of time required to develop expert-level pattern recognition skills. Further exploration is needed to determine how these approaches can be seamlessly and successfully integrated to optimize dermoscopy education., Competing Interests: Competing Interests: None., (©2022 Tran et al.)

Studies of cost and value can inform educational decision making, yet our understanding of the barriers to such research is incomplete. To address this gap, our aim was to explore the attitudes of global thought leaders in HPE towards cost and value research. This was a qualitative virtual interview study underpinned by social constructionism. In telephone or videoconference interviews in 2018-2019, we asked global healthcare professional thought leaders their views regarding HPE cost and value research, outstanding research questions in this area and why addressing these questions was important. Analysis was inductive and thematic, and incorporated review and comments from the original interviewees (member checking). We interviewed 11 thought leaders, nine of whom gave later feedback on our data interpretation (member checking). We identified four themes: Cost research is really important but potentially risky (quantifying and reporting costs provides evidence for decision-making but could lead to increased accountability and loss of autonomy); I don't have the knowledge and skills (lack of economic literacy); it's not what I went into education research to do (professional identity); and it's difficult to generate generalizable findings (the importance of context). This study contributes to a wider conversation in the literature about cost and value research by bringing in the views of global HPE thought leaders. Our findings provide insight to inform how best to engage and empower educators and researchers in the processes of asking and answering meaningful, acceptable and relevant cost and value questions in HPE., (© 2022. The Author(s), under exclusive licence to Springer Nature B.V.)

Introduction: During a health crisis, hospitals must prioritise activities and resources, which can compromise clerkship-based learning. We explored how health crises affect clinical clerkships using the COVID-19 pandemic as an example., Methods: In a constructivist qualitative study, we conducted 22 semi-structured interviews with key stakeholders (i.e. medical students and doctors) from two teaching hospitals and 10 different departments. We used thematic analysis to investigate our data and used stakeholder theory as a sensitising concept., Results: We identified three themes: (1) emotional triggers and reactions; (2) negotiation of legitimacy; and (3) building resilience. Our results suggest that the health crisis accentuated already existing problems in clerkships, such as students' feelings of low legitimacy, constant negotiation of roles, inconsistencies navigating rules and regulations and low levels of active participation. Medical students and doctors adapted to the new organisational demands by developing increased resilience. Students responded by reaching out for guidance and acceptance to remain relevant in the clinical clerkships. Doctors developed a behaviour of closing in and focused on managing themselves and their patients. This created tension between these two stakeholder groups., Conclusion: A health crisis can critically disrupt the hierarchical structure within the clinical clerkships and exacerbate existing conflicts between stakeholder groups. When medical students are not perceived as legitimate stakeholders in clinical clerkships during a health crisis, their attendance is perceived as unnecessary or even a nuisance. Despite increased student proactiveness and resilience, their roles inevitably shift from being doctors-to-be to students-to-be-managed., (© 2022 The Authors. Medical Education published by Association for the Study of Medical Education and John Wiley & Sons Ltd.)

The purpose of this scoping review was to explore how errors are conceptualized in medical education contexts by examining different error perspectives and practices. This review used a scoping methodology with a systematic search strategy to identify relevant studies, written in English, and published before January 2021. Four medical education journals (Medical Education, Advances in Health Science Education, Medical Teacher, and Academic Medicine) and four clinical journals (Journal of the American Medical Association, Journal of General Internal Medicine, Annals of Surgery, and British Medical Journal) were purposively selected. Data extraction was charted according to a data collection form. Of 1505 screened studies, 79 studies were included. Three overarching perspectives were identified: 'understanding errors') (n = 31), 'avoiding errors' (n = 25), 'learning from errors' (n = 23). Studies that aimed at'understanding errors' used qualitative methods (19/31, 61.3%) and took place in the clinical setting (19/31, 61.3%), whereas studies that aimed at 'avoiding errors' and 'learning from errors' used quantitative methods ('avoiding errors': 20/25, 80%, and 'learning from errors': 16/23, 69.6%, p = 0.007) and took place in pre-clinical (14/25, 56%) and simulated settings (10/23, 43.5%), respectively (p < 0.001). The three perspectives differed significantly in terms of inclusion of educational theory: 'Understanding errors' studies 16.1% (5/31),'avoiding errors' studies 48% (12/25), and 'learning from errors' studies 73.9% (17/23), p < 0.001. Errors in medical education and clinical practice are defined differently, which makes comparisons difficult. A uniform understanding is not necessarily a goal but improving transparency and clarity of how errors are currently conceptualized may improve our understanding of when, why, and how to use and learn from errors in the future., (© 2022. The Author(s), under exclusive licence to Springer Nature B.V.)

Background: Intestinal ultrasound (IUS) is a non-invasive modality for monitoring disease activity in inflammatory bowel diseases (IBD). IUS training currently lacks well-defined standards and international consensus on competency criteria., Aim: To achieve international consensus on what competencies should be expected from a newly certified IUS practitioner., Methods: A three-round, iterative Delphi process was conducted among 54 IUS experts from 17 countries. Round 1 was a brainstorming phase with an open-ended question to identify the knowledge and skills that experts believe a newly certified IUS practitioner should possess. The experts' suggestions were then organised into statements by a Steering Committee. In round 2, the experts commented upon and rated the statements, which were revised accordingly. In round 3, the experts rated the revised statements. Statements meeting the pre-defined consensus criterion of at least 70% agreement were included in the final list of statements., Results: In total, 858 items were suggested by the experts in the first round. Based on these suggestions, 55 statements were organised into three categories: knowledge, technical skills and interpretation skills. After the second round, 53 revised statements remained. After the final round, a total of 41 statements had achieved consensus., Conclusions: We established international, expert consensus on the knowledge and skills that should be expected from newly certified IUS practitioners. These consensus statements are the first step towards mastery learning for IUS training. Educators can utilise these statements to design training programmes and evaluate the competencies of trainees before they engage in independent practice., (© 2022 The Authors. Alimentary Pharmacology & Therapeutics published by John Wiley & Sons Ltd.)

Background: Collaborative skills learning in the form of dyad learning compared with individual learning has been shown to lead to non-inferior skills retention and transfer. However, we have limited knowledge on which learning activities improve collaborative skills training and how the number of collaborators may impact skills transfer. We explored the effects of skills training individually, in dyads, triads or tetrads on learning activities during training and on subsequent skills transfer., Methods: In a randomised, controlled study, participants completed a pre-post-transfer-test set-up in groups of one to four. Participants completed 2 hours of obstetric ultrasound training. In the dyad, triad and tetrad group participants took turns actively handling the ultrasound probe. All performances were rated by two blinded experts using the Objective Structured Assessment of Ultrasound Skills (OSAUS) scale and a Global Rating Scale (GRS). All training was video recorded, and learning activities were analysed using the Interactive-Constructive-Active-Passive (ICAP) framework., Results: One hundred one participants completed the simulation-based training, and ninety-seven completed the transfer test. Performance scores improved significantly from pre- to post-test for all groups (p < 0.001, ηp 2  = 0.55). However, group size did not affect transfer test performance on OSAUS scores (p = 0.13, ηp 2  = 0.06) or GRS scores (p = 0.23, ηp 2  = 0.05). ICAP analyses of training activities showed that time spent on non-learning and passive learning activities increased with group size (p < 0.001, ηp 2  = 0.31), whereas time spent on constructive and interactive learning activities was constant between groups compared with singles (p < 0.001, ηp 2  = 0.72)., Conclusion: Collaborative skills learning in groups of up to four did not impair skills transfer despite less hands-on time. This may be explained by a compensatory shift towards constructive and interactive learning activities that outweigh the effect of shorter hands-on time., (© 2022 The Authors. Medical Education published by Association for the Study of Medical Education and John Wiley & Sons Ltd.)

Background: Despite the widespread use of optical coherence tomography (OCT) for imaging of keratinocyte carcinoma, we lack an expert consensus on the characteristic OCT features of basal cell carcinoma (BCC), an internationally vetted set of OCT terms to describe various BCC subtypes, and an educational needs assessment., Objectives: To identify relevant BCC features in OCT images, propose terminology based on inputs from an expert panel and identify content for a BCC-specific curriculum for OCT trainees., Methods: Over three rounds, we conducted a Delphi consensus study on BCC features and terminology between March and September 2020. In the first round, experts were asked to propose BCC subtypes discriminable by OCT, provide OCT image features for each proposed BCC subtypes and suggest content for a BCC-specific OCT training curriculum. If agreement on a BCC-OCT feature exceeded 67%, the feature was accepted and included in a final review. In the second round, experts had to re-evaluate features with less than 67% agreement and rank the ten most relevant BCC OCT image features for superficial BCC, nodular BCC and infiltrative and morpheaphorm BCC subtypes. In the final round, experts received the OCT-BCC consensus list for a final review, comments and confirmation., Results: The Delphi included six key opinion leaders and 22 experts. Consensus was found on terminology for three OCT BCC image features: (i) hyporeflective areas, (ii) hyperreflective areas and (iii) ovoid structures. Further, the participants ranked the ten most relevant image features for nodular, superficial, infiltrative and morpheaform BCC. The target group and the key components for a curriculum for OCT imaging of BCC have been defined., Conclusion: We have established a set of OCT image features for BCC and preferred terminology. A comprehensive curriculum based on the expert suggestions will help implement OCT imaging of BCC in clinical and research settings., (© 2022 European Academy of Dermatology and Venereology.)

Objective: This systematic review aims to examine the use of standard-setting methods in the context of simulation-based training of surgical procedures., Summary of Background: Simulation-based training is increasingly used in surgical education. However, it is important to determine which level of competency trainees must reach during simulation-based training before operating on patients. Therefore, pass/fail standards must be established using systematic, transparent, and valid methods., Methods: Systematic literature search was done in 4 databases (Ovid MEDLINE, Embase, Web of Science, and Cochrane Library). Original studies investigating simulation-based assessment of surgical procedures with the application of a standard setting were included. Quality of evidence was appraised using GRADE., Results: Of 24,299 studies identified by searches, 232 studies met the inclusion criteria. Publications using already established standard settings were excluded (N = 70), resulting in 162 original studies included in the final analyses. Most studies described how the standard setting was determined (N = 147, 91%) and most used the mean or median performance score of experienced surgeons (n = 65, 40%) for standard setting. We found considerable differences across most of the studies regarding study design, setup, and expert level classification. The studies were appraised as having low and moderate evidence., Conclusion: Surgical education is shifting toward competency-based education, and simulation-based training is increasingly used for acquiring skills and assessment. Most studies consider and describe how standard settings are established using more or less structured methods but for current and future educational programs, a critical approach is needed so that the learners receive a fair, valid, and reliable assessment., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: Emergency caesarean sections (ECS) are time-sensitive procedures. Multiple factors may affect team efficiency but their relative importance remains unknown. This study aimed to identify the most important predictors contributing to quality of care during ECS in terms of the arrival-to-delivery interval., Design: A retrospective cohort study. ECS were classified by urgency using emergency categories one/two and three (delivery within 30 and 60 min). In total, 92 predictor variables were included in the analysis and grouped as follows: 'Maternal objective', 'Maternal psychological', 'Fetal factors', 'ECS Indication', 'Emergency category', 'Type of anaesthesia', 'Team member qualifications and experience' and 'Procedural'. Data was analysed with a linear regression model using elastic net regularisation and jackknife technique to improve generalisability. The relative influence of the predictors, percentage significant predictor weight (PSPW) was calculated for each predictor to visualise the main determinants of arrival-to-delivery interval., Setting and Participants: Patient records for mothers undergoing ECS between 2010 and 2017, Nordsjællands Hospital, Capital Region of Denmark., Primary Outcome Measures: Arrival-to-delivery interval during ECS., Results: Data was obtained from 2409 patient records for women undergoing ECS. The group of predictors representing 'Team member qualifications and experience' was the most important predictor of arrival-to-delivery interval in all ECS emergency categories (PSPW 25.9% for ECS category one/two; PSPW 35.5% for ECS category three). In ECS category one/two the 'Indication for ECS' was the second most important predictor group (PSPW 24.9%). In ECS category three, the second most important predictor group was 'Maternal objective predictors' (PSPW 24.2%)., Conclusion: This study provides empirical evidence for the importance of team member qualifications and experience relative to other predictors of arrival-to-delivery during ECS. Machine learning provides a promising method for expanding our current knowledge about the relative importance of different factors in predicting outcomes of complex obstetric events., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: Fast and accurate detection of SARS-CoV-2 is essential in limiting the COVID-19 pandemic. Rapid antigen (AG) tests provide results within minutes; however, their accuracy has been questioned. The study aims to determine the accuracy and cost of the STANDARD Q COVID-19 AG test compared with RT-PCR., Methods: Individuals 18 years or older with an appointment for a RT-PCR test on 26-31 December 2020 at a public test centre in Copenhagen, Denmark were invited to participate. An oropharyngeal swab was collected for RT-PCR analysis, followed by a nasopharyngeal swab examined by the AG test (SD Biosensor). The diagnostic accuracy of the AG test was calculated with RT-PCR as reference. Costs were evaluated for both tests., Results: A total of 4,811 paired conclusive test results were collected (median age: 45 years, female: 53%). The RT-PCR test revealed 221 (4.6%) positive tests. The overall sensitivity and specificity of the AG test were 69.7% and 99.5%, respectively. Viral cycle threshold values were significantly higher in individuals with false negative AG tests than in individuals who were true positives. The RT-PCR test and AG test costs were 67.0 DKK (10.8 USD) and 35.0 DKK (5.7 USD), respectively, per positive case detected at 100,000 daily tests., Conclusions: The AG test enables mass testing and provides immediate results, which is important in SARS-CoV-2 screening. The AG test is a good and relevant supplement to RT-PCR testing in public SARS-CoV-2 screenings., Funding: This project received no external funding. Copenhagen Medical A/S delivering the rapid AG tests and provided test personnel but were not otherwise involved., Trial Registration: Clinicaltrials.org: NCT04716088., (Articles published in the DMJ are “open access”. This means that the articles are distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits any non-commercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.)

Introduction: Dyad learning occurs when two students work together to acquire new skills and knowledge. Several studies have provided evidence to support the educational rationale for dyad learning in the controlled simulated setting. However, the role of dyad learning in the clinical setting remains uncertain. Unlike the simulated setting, learning in the clinical setting depends on a complex interplay between medical students, doctors, nurses and patients potentially making dyad learning less valuable in clerkships. The objective of this study was to explore how key stakeholders perceive the value of implementing dyad learning during medical students' clinical clerkships., Methods: In a constructivist qualitative study, we conducted 51 semi-structured interviews with 36 key stakeholders involved in dyad learning, including 10 medical students, 12 doctors, five nurses and nine patients. Data were coded inductively using thematic analysis, then coded deductively using stakeholder theory as a theoretical framework., Results: We found that stakeholders generally perceived the educational impact of dyad learning in the clinical setting similarly but disagreed on its value. Students emphasised that dyad learning made them participate more actively during patient encounters and patients did not mind having two students present. Doctors and nurses considered dyad learning disruptive to the balance between service and training and reported that it did not resonate with their perception of good patient care., Conclusion: Dyad learning enables students to be more active during their clinical clerkships, but it easily disrupts the balance between service and training. This disruption may be exacerbated by the shifted balance in priorities and values between different stakeholder groups, as well as by making implicit teaching obligations more explicit for supervising doctors and nurses. Consequently, implementing dyad learning may not be perceived as valuable by doctors and nurses in the clinical setting, regardless of its pedagogical rationale., (© 2020 John Wiley & Sons Ltd and The Association for the Study of Medical Education.)

Introduction: The objective of this study was to explore the association between detection of fetal growth restriction and maternal-, healthcare provider- and organizational factors., Material and Methods: A historical, observational, multicentre study. All women who gave birth to a child with a birthweight <2.3rd centile from 1 September 2012 to 31 August 2015 in Zealand, Denmark, were included. The population was identified through the Danish Fetal Medicine Database. Medical charts were reviewed to obtain data regarding maternal characteristics and information on the healthcare professionals. Date of authorization for the midwives and obstetricians involved was extracted from the Danish Health Authorization Registry. Multivariable Cox regression models were used to identify predictors of antenatal detection of fetal growth restriction, and analyses were adjusted for hospital, body mass index, parity, the presence of at least one risk factor and experience of the first midwife, number of midwife visits, number of visits to a doctor, the experience of the consultant midwife or the educational level of the doctor, the number of scans and gaps in continuity of midwife-care. Antenatal detection was defined as an ultrasound estimated fetal weight <2.3rd centile (corresponding to -2 standard deviations) prior to delivery., Results: Among 78 544 pregnancies, 3069 (3.9%) had a fetal growth restriction. Detection occurred in 31% of fetal growth-restricted pregnancies. Clinical experience (defined as years since graduation) of the first consultation midwife was positively associated with detection, with a hazard ratio [HR] of 1.15, 95% confidence interval [CI] 1.03-1.28), for every 10 years of additional experience. The hazard of detection increased with the number of midwife consultations (HR 1.15, 95% CI 1.05-1.26) and with multiparity (HR 1.28, 95% CI 1.03-1.58). After adjusting for all covariates, an unexplained difference between hospitals (P = .01) remained., Conclusions: The low-risk nullipara may constitute an overlooked group of women at increased risk of antenatal non-detection of fetal growth restriction. Being screened by experienced midwives during early pregnancy and having access to multiple midwife consultations may improve future diagnosis., (© 2020 Nordic Federation of Societies of Obstetrics and Gynecology.)

Context: A long-standing myth in medical education research is a divide between two different poles: research aiming to advance theory with little focus on practical applications ('ivory tower' research) and practically oriented research aiming to serve educators and decision-makers with little focus on advancing theory ('in-the-trenches' practice). We explored this myth in a sample of randomised medical education studies using Stokes' four-quadrant framework for the classification of research perspective., Methods: We searched MEDLINE, Embase, CINAHL, PsychINFO, ERIC, Web of Science and Scopus for studies in medical education using a randomised design that were published between 1 January 2018 and 31 December 2018. We used Stokes' four-quadrant framework to categorise the studies according to their use of theory, concepts and their justification for practical use. We compared medical education research published in medical education journals and clinical journals., Results: A total of 150 randomised studies were included in the analysis. The largest segment of studies (46.7%) was categorised as use-inspired basic research (Pasteur's Quadrant), closely followed by pure applied research (40.7%, Edison's Quadrant). Only a few studies were categorised as aiming to advance knowledge with no thought for practical educational application (2.0%, Bohr's Quadrant). The proportion of studies that included educational concepts and theory differed according to publication in clinical journals or medical education journals: 40.5% vs 71.8%, respectively, P < .001. There were no differences between journals with regard to the proportion of studies that included a practical educational or clinical rationale (P = .99)., Conclusion: In a large sample of studies using randomised designs, we found no evidence to support the myth that medical education research divides between two singular poles represented by 'ivory tower research' and 'in-the-trenches practice'. We did confirm prevailing assumptions regarding an emphasis on non-theoretical medical education research in clinical journals., (© 2020 John Wiley & Sons Ltd and The Association for the Study of Medical Education.)