OBJECTIVES: The development of External Quality Assessment Schemes (EQAS) for clinical flow cytometry (FCM) is challenging in the context of rare (immunological) diseases. Here, we introduce a novel EQAS monitoring the primary immunodeficiency Orientation Tube (PIDOT), developed by EuroFlow, in both a 'wet' and 'dry' format. This EQAS provides feedback on the quality of individual laboratories (i.e., accuracy, reproducibility and result interpretation), while eliminating the need for sample distribution. METHODS: In the wet format, marker staining intensities (MedFIs) within landmark cell populations in PIDOT analysis performed on locally collected healthy control (HC) samples, were compared to EQAS targets. In the dry format, participants analyzed centrally distributed PIDOT flow cytometry data (n=10). RESULTS: We report the results of six EQAS rounds across 20 laboratories in 11 countries. The wet format (212 HC samples) demonstrated consistent technical performance among laboratories (median %rCV on MedFIs=34.5 %; average failure rate 17.3 %) and showed improvement upon repeated participation. The dry format demonstrated effective proficiency of participants in cell count enumeration (range %rCVs 3.1-7.1 % for the major lymphoid subsets), and in identifying lymphoid abnormalities (79.3 % alignment with reference). CONCLUSIONS: The PIDOT-EQAS allows laboratories, adhering to the standardized EuroFlow approach, to monitor interlaboratory variations without the need for sample distribution, and provides them educational support to recognize rare clinically relevant immunophenotypic patterns of primary immunodeficiencies (PID). This EQAS contributes to quality improvement of PID diagnostics and can serve as an example for future flow cytometry EQAS in the context of rare diseases.
PURPOSE: Minimally invasive surgery (MIS) in neonates and infants presents technical challenges and is still unfamiliar to many paediatrics surgeons. This study aims to identify currently available simulators for neonatal/infant MIS training, to assess their validity, level of evidence, and related recommendations. METHODS: The review followed PRISMA guidelines and was registered in PROSPERO (CRD420250581050). Electronic search limited to English articles was performed through PubMed/MEDLINE, SCOPUS, Web of Science and Cochrane Database from January 2010 to June 2024. RESULTS: Out of 1084 identified records, 72 studies met the inclusion criteria and were analysed across general, gastrointestinal, thoracic, and urological MIS specialties. Recent efforts have led to the development of 3D-printed, animal-based, and hybrid models several of which showed high fidelity, skill differentiation, and educational value. Despite promising results, no universal MIS training model exists for neonate/infant patients, highlighting the need for structured, proficiency-based curricula. Overall, studies demonstrated moderate levels of evidence and recommendation, supporting integration of cost-effective simulation into paediatrics MIS training CONCLUSION: This systematic review highlights the need for validated, standardized simulation models and proficiency-based curricula to optimize neonate and infant MIS training and guide future research toward improving model fidelity, accessibility, and long-term educational outcomes.
- MeSH
- Clinical Competence MeSH
- Infant MeSH
- Humans MeSH
- Minimally Invasive Surgical Procedures * education MeSH
- Infant, Newborn MeSH
- Pediatrics * education MeSH
- Simulation Training * methods MeSH
- Check Tag
- Infant MeSH
- Humans MeSH
- Infant, Newborn MeSH
- Publication type
- Journal Article MeSH
- Systematic Review MeSH
Peroral endoscopic myotomy (POEM) is an advanced endoscopic procedure that has become a first-line treatment for esophageal achalasia and other esophageal spastic disorders. Structured training is essential to optimize the outcomes of this technique. The European Society of Gastrointestinal Endoscopy (ESGE) has recognized the need to formalize and enhance training in POEM. This Position Statement presents the results of a systematic review of the literature and a formal Delphi process, providing recommendations for an optimal training program in POEM that aims to produce endoscopists competent in this procedure. In a separate document (POEM curriculum Part II), we provide technical guidance on how to perform the POEM procedure based on the best available evidence. 1: POEM trainees should acquire a comprehensive theoretical knowledge of achalasia and other esophageal motility disorders that encompasses pathophysiology, diagnostic tool proficiency, clinical outcome assessment, potential adverse events, and periprocedural management. 2: Experience in advanced endoscopic procedures (endoscopic mucosal resection and/or endoscopic submucosal dissection [ESD]) is encouraged as a beneficial prerequisite for POEM training. 3: ESGE suggests that POEM trainees without ESD experience should perform an indicative minimum number of 20 cases on ex vivo or animal models before advancing to human POEM cases with an experienced trainer. 4: ESGE recommends that the trainee should observe an indicative minimum number of 20 live cases at expert centers before starting to perform POEM in humans. 5: The trainee should undertake an indicative minimum number of 10 cases under expert supervision for the initial human POEM procedures, ensuring that trainees can complete all POEM steps independently. 6: ESGE recommends avoiding complex POEM cases during the early training phase. 7: POEM competence should reflect the technical success rate, both the short- and long-term clinical success rates, and the rate of true adverse events. 8: A POEM center should maintain a prospective registry of all procedures performed, including patient work-up and outcomes, procedural techniques, and adverse events.
- MeSH
- Esophageal Achalasia * surgery MeSH
- Delphi Technique MeSH
- Natural Orifice Endoscopic Surgery * education MeSH
- Endoscopy, Gastrointestinal * education MeSH
- Clinical Competence MeSH
- Curriculum * MeSH
- Humans MeSH
- Myotomy * education methods MeSH
- Pyloromyotomy * education MeSH
- Societies, Medical MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Consensus Development Conference MeSH
- Systematic Review MeSH
- Geographicals
- Europe MeSH
Idiopathic inflammatory myopathies (IIM), or myositis, are a heterogeneous group of systemic autoimmune disorders that are associated with significant morbidity and mortality. Conducting high-quality clinical trials in IIM is challenging due to the rare and variable presentations of disease. To address this challenge, the Myositis Clinical Trials Consortium (MCTC) was formed. MCTC is a collaborative international alliance dedicated to facilitating, promoting, coordinating and conducting clinical trials and related research in IIM. This partnership works to advance the discovery of effective evidence-based treatments for IIM by integrating a diverse group of clinical investigators, research professionals, medical centres, patient groups, and industry partners. The Steering Committee, Core Group, and Paediatric Subcommittee of MCTC are comprised of myositis experts and junior investigators from around the world, representing a diversity of genders, geographies, and subspecialties. MCTC works alongside other current myositis organisations to complement existing work by concentrating on the operationalisation of clinical trials. Our pilot Myositis Investigators' Information Survey gathered responses from 173 myositis investigators globally and found considerable variability in proficiency with outcome measures, geographic disparities in patient recruitment, and a significant disconnect between investigators' routine myositis patient load and clinical trial enrolment. MCTC will meet the need to support and diversify myositis clinical trials by facilitating trial planning, feasibility assessments, site selection, and the training and mentoring of junior investigators/centres to establish their readiness for clinical trial participation. Through experienced leadership, strategic collaborations, and interdisciplinary discussions, MCTC will establish standards for IIM clinical trial design, protocols, and outcome measures in myositis.
- MeSH
- Child MeSH
- Adult MeSH
- Clinical Trials as Topic * MeSH
- Cooperative Behavior MeSH
- Humans MeSH
- International Cooperation * MeSH
- Adolescent MeSH
- Myositis * therapy diagnosis MeSH
- Research Design MeSH
- Check Tag
- Child MeSH
- Adult MeSH
- Humans MeSH
- Adolescent MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
This article delves into the dynamics of human interaction with artificial intelligence (AI), emphasizing the optimization of these interactions to enhance human productivity. Employing a Grounded Theory Literature Review (GTLR) methodology, the study systematically identifies and analyzes themes from literature published between 2018 and 2023. Data were collected primarily from the Scopus database, with the Web of Science used to corroborate findings and include additional sources identified through a snowball effect. At the heart of this exploration is the pivotal role of socio-emotional attributes such as trust, empathy, rapport, user engagement, and anthropomorphization-elements crucial for the successful integration of AI into human activities. By conducting a comprehensive review of existing literature and incorporating case studies, this study illuminates how AI systems can be designed and employed to foster deeper trust and empathetic understanding between humans and machines. The analysis reveals that when AI systems are attuned to human emotional and cognitive needs, there is a marked improvement in collaborative efficiency and productivity. Furthermore, the paper discusses the ethical implications and potential societal impacts of fostering such human-AI relationships. It argues for a paradigm shift in AI development-from focusing predominantly on technical proficiency to embracing a more holistic approach that values the socio-emotional aspects of human-AI interaction. This shift could pave the way for more meaningful and productive collaborations between humans and AI, ultimately leading to advancements that are both technologically innovative and human-centric.
- Publication type
- Journal Article MeSH
- Review MeSH
PURPOSE: With the global epidemic of obesity, the importance of metabolic and bariatric surgery (MBS) is greater than ever before. Performing these surgeries requires academic training and the completion of a dedicated fellowship training program. This study aimed to develop guidelines based on expert consensus using a modified Delphi method to create the criteria for metabolic and bariatric surgeons that must be mastered before obtaining privileges to perform MBS. METHODS: Eighty-nine recognized MBS surgeons from 42 countries participated in the Modified Delphi consensus to vote on 30 statements in two rounds. An agreement/disagreement among ≥ 70.0% of the experts was regarded to indicate a consensus. RESULTS: Consensus was reached on 29 out of 30 statements. Most experts agreed that before getting privileges to perform MBS, surgeons must hold a general surgery degree and complete or have completed a dedicated fellowship training program. The experts agreed that the learning curves for the various operative procedures are approximately 25-50 operations for the LSG, 50-75 for the OAGB, and 75-100 for the RYGB. 93.1% of experts agreed that MBS surgeons should diligently record patients' data in their National or Global database. CONCLUSION: MBS surgeons should have a degree in general surgery and have been enrolled in a dedicated fellowship training program with a structured curriculum. The learning curve of MBS procedures is procedure dependent. MBS surgeons must demonstrate proficiency in managing postoperative complications, collaborate within a multidisciplinary team, commit to a minimum 2-year patient follow-up, and actively engage in national and international MBS societies.
- MeSH
- Bariatric Surgery * standards education MeSH
- Surgeons standards education MeSH
- Delphi Technique * MeSH
- Clinical Competence standards MeSH
- Consensus * MeSH
- Learning Curve MeSH
- Humans MeSH
- Obesity, Morbid surgery MeSH
- Fellowships and Scholarships standards MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
INTRODUCTION: Surgical training traditionally adheres to the apprenticeship paradigm, potentially exposing trainees to an increased risk of complications stemming from their limited experience. To mitigate this risk, augmented and virtual reality have been considered, though their effectiveness is difficult to assess. RESEARCH QUESTION: The PASSION study seeks to investigate the improvement of manual dexterity following intensive training with neurosurgical simulators and to discern how surgeons' psychometric characteristics may influence their learning process and surgical performance. MATERIAL AND METHODS: Seventy-two residents were randomized into the simulation group (SG) and control group (CG). The course spanned five days, commencing with assessment of technical skills in basic procedures within a wet-lab setting on day 1. Over the subsequent core days, the SG engaged in simulated procedures, while the CG carried out routine activities in an OR. On day 5, all residents' technical competencies were evaluated. Psychometric measures of all participants were subjected to analysis. RESULTS: The SG demonstrated superior performance (p < 0.0001) in the brain tumour removal compared to the CG. Positive learning curves were evident in the SG across the three days of simulator-based training for all tumour removal tasks (all p-values <0.05). No significant differences were noted in other tasks, and no meaningful correlations were observed between performance and any psychometric parameters. DISCUSSION AND CONCLUSION: A brief and intensive training regimen utilizing 3D virtual reality simulators enhances residents' microsurgical proficiency in brain tumour removal models. Simulators emerge as a viable tool to expedite the learning curve of in-training neurosurgeons.
- Publication type
- Journal Article MeSH
