Generative-AI (GAI) models like ChatGPT are becoming widely discussed and utilized tools in medical education. For example, it can be used to assist with studying for exams, shown capable of passing the USMLE board exams. However, there have been concerns expressed regarding its fair and ethical use. We designed an electronic survey for students across North American medical colleges to gauge their views on and current use of ChatGPT and similar technologies in May, 2023. Overall, 415 students from at least 28 medical schools completed the questionnaire and 96% of respondents had heard of ChatGPT and 52% had used it for medical school coursework. The most common use in pre-clerkship and clerkship phase was asking for explanations of medical concepts and assisting with diagnosis/treatment plans, respectively. The most common use in academic research was for proof reading and grammar edits. Respondents recognized the potential limitations of ChatGPT, including inaccurate responses, patient privacy, and plagiarism. Students recognized the importance of regulations to ensure proper use of this novel technology. Understanding the views of students is essential to crafting workable instructional courses, guidelines, and regulations that ensure the safe, productive use of generative-AI in medical school.

BACKGROUND: Generative Pretrained Model (GPT) chatbots have gained popularity since the public release of ChatGPT. Studies have evaluated the ability of different GPT models to provide information about medical conditions. To date, no study has assessed the quality of ChatGPT outputs to prostate cancer related questions from both the physician and public perspective while optimizing outputs for patient consumption. METHODS: Nine prostate cancer-related questions, identified through Google Trends (Global), were categorized into diagnosis, treatment, and postoperative follow-up. These questions were processed using ChatGPT 3.5, and the responses were recorded. Subsequently, these responses were re-inputted into ChatGPT to create simplified summaries understandable at a sixth-grade level. Readability of both the original ChatGPT responses and the layperson summaries was evaluated using validated readability tools. A survey was conducted among urology providers (urologists and urologists in training) to rate the original ChatGPT responses for accuracy, completeness, and clarity using a 5-point Likert scale. Furthermore, two independent reviewers evaluated the layperson summaries on correctness trifecta: accuracy, completeness, and decision-making sufficiency. Public assessment of the simplified summaries' clarity and understandability was carried out through Amazon Mechanical Turk (MTurk). Participants rated the clarity and demonstrated their understanding through a multiple-choice question. RESULTS: GPT-generated output was deemed correct by 71.7% to 94.3% of raters (36 urologists, 17 urology residents) across 9 scenarios. GPT-generated simplified layperson summaries of this output was rated as accurate in 8 of 9 (88.9%) scenarios and sufficient for a patient to make a decision in 8 of 9 (88.9%) scenarios. Mean readability of layperson summaries was higher than original GPT outputs ([original ChatGPT v. simplified ChatGPT, mean (SD), p-value] Flesch Reading Ease: 36.5(9.1) v. 70.2(11.2), <0.0001; Gunning Fog: 15.8(1.7) v. 9.5(2.0), p < 0.0001; Flesch Grade Level: 12.8(1.2) v. 7.4(1.7), p < 0.0001; Coleman Liau: 13.7(2.1) v. 8.6(2.4), 0.0002; Smog index: 11.8(1.2) v. 6.7(1.8), <0.0001; Automated Readability Index: 13.1(1.4) v. 7.5(2.1), p < 0.0001). MTurk workers (n = 514) rated the layperson summaries as correct (89.5-95.7%) and correctly understood the content (63.0-87.4%). CONCLUSION: GPT shows promise for correct patient education for prostate cancer-related contents, but the technology is not designed for delivering patients information. Prompting the model to respond with accuracy, completeness, clarity and readability may enhance its utility when used for GPT-powered medical chatbots.

Annually, about 300 million surgeries lead to significant intraoperative adverse events (iAEs), impacting patients and surgeons. Their full extent is underestimated due to flawed assessment and reporting methods. Inconsistent adoption of new grading systems and a lack of standardization, along with litigation concerns, contribute to underreporting. Only half of relevant journals provide guidelines on reporting these events, with a lack of standards in surgical literature. To address these issues, the Intraoperative Complications Assessment and Reporting with Universal Standard (ICARUS) Global Surgical Collaboration was established in 2022. The initiative involves conducting global surveys and a Delphi consensus to understand the barriers for poor reporting of iAEs, validate shared criteria for reporting, define iAEs according to surgical procedures, evaluate the existing grading systems’ reliability, and identify strategies for enhancing the collection, reporting, and management of iAEs. Invitation to participate are extended to all the surgical specialties, interventional cardiology, interventional radiology, OR Staffs and anesthesiology. This effort represents an essential step towards improved patient safety and the well-being of healthcare professionals in the surgical field.

Numerous MRI-based artificial intelligence (AI) frameworks have been designed for prostate cancer lesion detection, segmentation, and classification via MRI as a result of intrareader and interreader variability that is inherent to traditional interpretation. Open-source data sets have been released with the intention of providing freely available MRIs for the testing of diverse AI frameworks in automated or semiautomated tasks. Here, an in-depth assessment of the performance of MRI-based AI frameworks for detecting, segmenting, and classifying prostate lesions using open-source databases was performed. Among 17 data sets, 12 were specific to prostate cancer detection/classification, with 52 studies meeting the inclusion criteria.

BACKGROUND:Inguinal lymph node dissection plays an important role in the management of melanoma, penile and vulval cancer. Inguinal lymph node dissection is associated with various intraoperative and postoperative complications with significant heterogeneity in classification and reporting. This lack of standardization challenges efforts to study and report inguinal lymph node dissection outcomes. The aim of this study was to devise a system to standardize the classification and reporting of inguinal lymph node dissection perioperative complications by creating a worldwide collaborative, the complications and adverse events in lymphadenectomy of the inguinal area (CALI) group. METHODS:A modified 3-round Delphi consensus approach surveyed a worldwide group of experts in inguinal lymph node dissection for melanoma, penile and vulval cancer. The group of experts included general surgeons, urologists and oncologists (gynaecological and surgical). The survey assessed expert agreement on inguinal lymph node dissection perioperative complications. Panel interrater agreement and consistency were assessed as the overall percentage agreement and Cronbach's α. RESULTS:Forty-seven experienced consultants were enrolled: 26 (55.3%) urologists, 11 (23.4%) surgical oncologists, 6 (12.8%) general surgeons and 4 (8.5%) gynaecology oncologists. Based on their expertise, 31 (66%), 10 (21.3%) and 22 (46.8%) of the participants treat penile cancer, vulval cancer and melanoma using inguinal lymph node dissection respectively; 89.4% (42 of 47) agreed with the definitions and inclusion as part of the inguinal lymph node dissection intraoperative complication group, while 93.6% (44 of 47) agreed that postoperative complications should be subclassified into five macrocategories. Unanimous agreement (100%, 37 of 37) was achieved with the final standardized classification system for reporting inguinal lymph node dissection complications in melanoma, vulval cancer and penile cancer. CONCLUSION:The complications and adverse events in lymphadenectomy of the inguinal area classification system has been developed as a tool to standardize the assessment and reporting of complications during inguinal lymph node dissection for the treatment of melanoma, vulval and penile cancer.

OBJECTIVES:To determine the extent and content of academic publishers' and scientific journals' guidance for authors on the use of generative artificial intelligence (GAI). DESIGN:Cross sectional, bibliometric study. SETTING:Websites of academic publishers and scientific journals, screened on 19-20 May 2023, with the search updated on 8-9 October 2023. PARTICIPANTS:Top 100 largest academic publishers and top 100 highly ranked scientific journals, regardless of subject, language, or country of origin. Publishers were identified by the total number of journals in their portfolio, and journals were identified through the Scimago journal rank using the Hirsch index (H index) as an indicator of journal productivity and impact. MAIN OUTCOME MEASURES:The primary outcomes were the content of GAI guidelines listed on the websites of the top 100 academic publishers and scientific journals, and the consistency of guidance between the publishers and their affiliated journals. RESULTS:Among the top 100 largest publishers, 24% provided guidance on the use of GAI, of which 15 (63%) were among the top 25 publishers. Among the top 100 highly ranked journals, 87% provided guidance on GAI. Of the publishers and journals with guidelines, the inclusion of GAI as an author was prohibited in 96% and 98%, respectively. Only one journal (1%) explicitly prohibited the use of GAI in the generation of a manuscript, and two (8%) publishers and 19 (22%) journals indicated that their guidelines exclusively applied to the writing process. When disclosing the use of GAI, 75% of publishers and 43% of journals included specific disclosure criteria. Where to disclose the use of GAI varied, including in the methods or acknowledgments, in the cover letter, or in a new section. Variability was also found in how to access GAI guidelines shared between journals and publishers. GAI guidelines in 12 journals directly conflicted with those developed by the publishers. The guidelines developed by top medical journals were broadly similar to those of academic journals. CONCLUSIONS:Guidelines by some top publishers and journals on the use of GAI by authors are lacking. Among those that provided guidelines, the allowable uses of GAI and how it should be disclosed varied substantially, with this heterogeneity persisting in some instances among affiliated publishers and journals. Lack of standardization places a burden on authors and could limit the effectiveness of the regulations. As GAI continues to grow in popularity, standardized guidelines to protect the integrity of scientific output are needed.

Inguinal lymph node dissection (ILND) plays a crucial role in the oncological management of patients with melanoma, penile, and vulvar cancer. This study aims to systematically evaluate perioperative adverse events (AEs) in patients undergoing ILND and its reporting. A systematic review was conducted according to PRISMA. PubMed, MEDLINE, Scopus, and Embase were queried to identify studies discussing perioperative AEs in patients with melanoma, penile, and vulvar cancer following ILND. Our search generated 3.469 publications, with 296 studies meeting the inclusion criteria. Details of 14.421 patients were analyzed. Of these studies, 58 (19.5%) described intraoperative AEs (iAEs) as an outcome of interest. Overall, 68 (2.9%) patients reported at least one iAE. Postoperative AEs were reported in 278 studies, combining data on 10.898 patients. Overall, 5.748 (52.7%) patients documented ≥1 postoperative AEs. The most reported ILND-related AEs were lymphatic AEs, with a total of 4.055 (38.8%) events. The pooled meta-analysis confirmed that high BMI (RR 1.09; p = 0.006), ≥1 comorbidities (RR 1.79; p = 0.01), and diabetes (RR 1.81; p =  < 0.00001) are independent predictors for any AEs after ILND. When assessing the quality of the AEs reporting, we found 25% of studies reported at least 50% of the required criteria. ILND performed in melanoma, penile, and vulvar cancer patients is a morbid procedure. The quality of the AEs reporting is suboptimal. A more standardized AEs reporting system is needed to produce comparable data across studies for furthering the development of strategies to decrease AEs.

INTRODUCTION:The accurate assessment and grading of adverse events (AE) is essential to ensure comparisons between surgical procedures and outcomes. The current lack of a standardized severity grading system may limit our understanding of the true morbidity attributed to AEs in surgery. The aim of this study is to review the prevalence in which intraoperative adverse event (iAE) severity grading systems are used in the literature, evaluate the strengths and limitations of these systems, and appraise their applicability in clinical studies.METHODS:A systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines. PubMed, Web of Science, and Scopus were queried to yield all clinical studies reporting the proposal and/or the validation of iAE severity grading systems. Google Scholar, Web of Science, and Scopus were searched separately to identify the articles citing the systems to grade iAEs identified in the first search.RESULTS:Our search yielded 2957 studies, with 7 studies considered for the qualitative synthesis. Five studies considered only surgical/interventional iAEs, while 2 considered both surgical/interventional and anesthesiologic iAEs. Two included studies validated the iAE severity grading system prospectively. A total of 357 citations were retrieved, with an overall self/nonself-citation ratio of 0.17 (53/304). The majority of citing articles were clinical studies (44.1%). The average number of citations per year was 6.7 citations for each classification/severity system, with only 2.05 citations/year for clinical studies. Of the 158 clinical studies citing the severity grading systems, only 90 (56.9%) used them to grade the iAEs. The appraisal of applicability (mean%/median%) was below the 70% threshold in 3 domains: stakeholder involvement (46/47), clarity of presentation (65/67), and applicability (57/56).CONCLUSION:Seven severity grading systems for iAEs have been published in the last decade. Despite the importance of collecting and grading the iAEs, these systems are poorly adopted, with only a few studies per year using them. A uniform globally implemented severity grading system is needed to produce comparable data across studies and develop strategies to decrease iAEs, further improving patient safety.

INTRODUCTION:This study assessed ChatGPT's ability to generate readable, accurate, and clear layperson summaries of urological studies, and compared the performance of ChatGPT-generated summaries with original abstracts and author-written patient summaries to determine its effectiveness as a potential solution for creating accessible medical literature for the public. METHODS:Articles from the top 5 ranked urology journals were selected. A ChatGPT prompt was developed following guidelines to maximize readability, accuracy, and clarity, minimizing variability. Readability scores and grade-level indicators were calculated for the ChatGPT summaries, original abstracts, and patient summaries. Two MD physicians independently rated the accuracy and clarity of the ChatGPT-generated layperson summaries. Statistical analyses were conducted to compare readability scores. Cohen's κ coefficient was used to assess interrater reliability for correctness and clarity evaluations. RESULTS:A total of 256 journal articles were included. The ChatGPT-generated summaries were created with an average time of 17.5 (SD 15.0) seconds. The readability scores of the ChatGPT-generated summaries were significantly better than the original abstracts, with Global Readability Score 54.8 (12.3) vs 29.8 (18.5), Flesch Kincade Reading Ease 54.8 (12.3) vs 29.8 (18.5), Flesch Kincaid Grade Level 10.4 (2.2) vs 13.5 (4.0), Gunning Fog Score 12.9 (2.6) vs 16.6 (4.1), Smog Index 9.1 (2.0) vs 12.0 (3.0), Coleman Liau Index 12.9 (2.1) vs 14.9 (3.7), and Automated Readability Index 11.1 (2.5) vs 12.0 (5.7; P < .0001 for all except Automated Readability Index, which was P = .037). The correctness rate of ChatGPT outputs was >85% across all categories assessed, with interrater agreement (Cohen's κ) between 2 independent physician reviewers ranging from 0.76-0.95. CONCLUSIONS:ChatGPT can create accurate summaries of scientific abstracts for patients, with well-crafted prompts enhancing user-friendliness. Although the summaries are satisfactory, expert verification is necessary for improved accuracy.

Journal Article Criteria for enhancing reporting of perioperative transfusions in surgical and anaesthesiological studies Get access Michael B Eppler, Michael B Eppler USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA https://orcid.org/0000-0001-6336-5857 Search for other works by this author on: Oxford Academic Google Scholar Conner Ganjavi, Conner Ganjavi USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar Ryan Davis, Ryan Davis USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar Aref S Sayegh, Aref S Sayegh USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar Jacob S Hershenhouse, Jacob S Hershenhouse USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar Daniel Mokhtar, Daniel Mokhtar USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar J Everett Knudsen, J Everett Knudsen USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar John Tran, John Tran USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar Lokesh Bhardwaj, Lokesh Bhardwaj USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar John J S Shin, John J S Shin USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar ... Show more Sij Hemal, Sij Hemal USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar Mitchell G Goldenberg, Mitchell G Goldenberg USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar Gus Miranda, Gus Miranda USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar Rene Sotelo, Rene Sotelo USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar Mihir Desai, Mihir Desai USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar Inderbir Gill, Inderbir Gill USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Search for other works by this author on: Oxford Academic Google Scholar Giovanni E Cacciamani Giovanni E Cacciamani USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA Correspondence to: Giovanni E. Cacciamani, USC Institute of Urology Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, 1441 Eastlake Ave, NOR 7416, Los Angeles, CA 90033-9178, USA (e-mail: Giovanni.cacciamani@med.usc.edu) Search for other works by this author on: Oxford Academic Google Scholar British Journal of Surgery, znad235, https://doi.org/10.1093/bjs/znad235 Published: 26 July 2023 Article history Received: 06 April 2023 Revision received: 09 June 2023 Accepted: 08 July 2023 Published: 26 July 2023

The swift progress and ubiquitous adoption of Generative AI (GAI), Generative Pre-trained Transformers (GPTs), and large language models (LLMs) like ChatGPT, have spurred queries about their ethical application, use, and disclosure in scholarly research and scientific productions. A few publishers and journals have recently created their own sets of rules; however, the absence of a unified approach may lead to a 'Babel Tower Effect,' potentially resulting in confusion rather than desired standardization. In response to this, we present the ChatGPT, Generative Artificial Intelligence, and Natural Large Language Models for Accountable Reporting and Use Guidelines (CANGARU) initiative, with the aim of fostering a cross-disciplinary global inclusive consensus on the ethical use, disclosure, and proper reporting of GAI/GPT/LLM technologies in academia. The present protocol consists of four distinct parts: a) an ongoing systematic review of GAI/GPT/LLM applications to understand the linked ideas, findings, and reporting standards in scholarly research, and to formulate guidelines for its use and disclosure, b) a bibliometric analysis of existing author guidelines in journals that mention GAI/GPT/LLM, with the goal of evaluating existing guidelines, analyzing the disparity in their recommendations, and identifying common rules that can be brought into the Delphi consensus process, c) a Delphi survey to establish agreement on the items for the guidelines, ensuring principled GAI/GPT/LLM use, disclosure, and reporting in academia, and d) the subsequent development and dissemination of the finalized guidelines and their supplementary explanation and elaboration documents.

With an ageing population, a higher percentage of surgical procedures will be performed in older patients carrying a greater number of co-morbidities 1 .The anatomical region of interest may no longer be a 'naive' surgical field.The results of chronic disease, previous or ongoing infection, past trauma, or previous surgery and other treatments (that is surgical radiation and chemotherapy) all have the potential to alter the anatomy at a surgical site and impact the surgical field for a given procedure.Notably, no systematic reporting or universal assessment of this exists in the literature.No two previous surgical site insults are the same.For instance, consider two patients undergoing a cholecystectomy.The first patient has a 2-year history of mild right-upper quadrant pain secondary to cholelithiasis not requiring surgical treatment.The second patient has a history of chronic cholecystitis.The cholecystectomy in the first patient is more likely to be a straightforward case.There is unlikely to be adhesions or abnormal anatomy simply from cholelithiasis.The cholecystectomy in the second patient is more likely to be a complicated procedure, as adhesions and/

Intraoperative adverse events (iAEs) impact the outcomes of surgery, and yet are not routinely collected, graded, and reported. Advancements in artificial intelligence (AI) have the potential to power real-time, automatic detection of these events and disrupt the landscape of surgical safety through the prediction and mitigation of iAEs. We sought to understand the current implementation of AI in this space. A literature review was performed to PRISMA-DTA standards. Included articles were from all surgical specialties and reported the automatic identification of iAEs in real-time. Details on surgical specialty, adverse events, technology used for detecting iAEs, AI algorithm/validation, and reference standards/conventional parameters were extracted. A meta-analysis of algorithms with available data was conducted using a hierarchical summary receiver operating characteristic curve (ROC). The QUADAS-2 tool was used to assess the article risk of bias and clinical applicability. A total of 2982 studies were identified by searching PubMed, Scopus, Web of Science, and IEEE Xplore, with 13 articles included for data extraction. The AI algorithms detected bleeding (n = 7), vessel injury (n = 1), perfusion deficiencies (n = 1), thermal damage (n = 1), and EMG abnormalities (n = 1), among other iAEs. Nine of the thirteen articles described at least one validation method for the detection system; five explained using cross-validation and seven divided the dataset into training and validation cohorts. Meta-analysis showed the algorithms were both sensitive and specific across included iAEs (detection OR 14.74, CI 4.7–46.2). There was heterogeneity in reported outcome statistics and article bias risk. There is a need for standardization of iAE definitions, detection, and reporting to enhance surgical care for all patients. The heterogeneous applications of AI in the literature highlights the pluripotent nature of this technology. Applications of these algorithms across a breadth of urologic procedures should be investigated to assess the generalizability of these data.

Artificial intelligence (AI) is being used in a variety of ways to improve healthcare research. AI algorithms can be used to analyze large amounts of medical data, such as patient records and clinical trial results, to identify trends and patterns that can help researchers better understand diseases and develop new treatments.

1 Department of Urology, Yale School of Medicine, New Haven, CT, USA; Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA; 2 Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA; Department of Ophthalmology, NYU Grossman School of Medicine, New York, NY, USA; Department of Pathology, Cleveland Clinic, Cleveland, OH, USA; 3 Cleveland Clinic Sustainability, Cleveland, OH, USA; 4 Mount Sinai Hospital, Miami, FL, USA; 5 The Lahey Clinic, Burlington, MA, USA; 6 Department of Urology, New York University Langone Health, New York, NY, USA; Departments of Urology and Population Health, New York University Langone Health, New York, NY, USA; Manhattan Veterans Affairs Medical Center, New York, NY, USA; 7 Environmental Genome Initiative, Raleigh, NC, USA; 8 Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA; Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA

BACKGROUND:Since its release in November 2022, ChatGPT has captivated society and shown potential for various aspects of health care. OBJECTIVE:To investigate potential use of ChatGPT, a large language model (LLM), in urology by gathering opinions from urologists worldwide. DESIGN, SETTING, AND PARTICIPANTS:An open web-based survey was distributed via social media and e-mail chains to urologists between April 20, 2023 and May 5, 2023. Participants were asked to answer questions related to their knowledge and experience with artificial intelligence, as well as their opinions of potential use of ChatGPT/LLMs in research and clinical practice. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:Data are reported as the mean and standard deviation for continuous variables, and the frequency and percentage for categorical variables. Charts and tables are used as appropriate, with descriptions of the chart types and the measures used. The data are reported in accordance with the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). RESULTS AND LIMITATIONS:A total of 456 individuals completed the survey (64% completion rate). Nearly half (47.7%) reported that they use ChatGPT/LLMs in their academic practice, with fewer using the technology in clinical practice (19.8%). More than half (62.2%) believe there are potential ethical concerns when using ChatGPT for scientific or academic writing, and 53% reported that they have experienced limitations when using ChatGPT in academic practice. CONCLUSIONS:Urologists recognise the potential of ChatGPT/LLMs in research but have concerns regarding ethics and patient acceptance. There is a desire for regulations and guidelines to ensure appropriate use. In addition, measures should be taken to establish rules and guidelines to maximise safety and efficiency when using this novel technology. PATIENT SUMMARY:A survey asked 456 urologists from around the world about using an artificial intelligence tool called ChatGPT in their work. Almost half of them use ChatGPT for research, but not many use it for patients care. The resonders think ChatGPT could be helpful, but they worry about problems like ethics and want rules to make sure it's used safely.

Introduction: Intraoperative adverse events (iAEs) occur and have the potential to impact the postoperative course. However, iAEs are underreported and are not routinely collected in the contemporary surgical literature. There is no widely utilized system for the collection of essential aspects of iAEs, and there is no established database for the standardization and dissemination of this data that likely have implications for outcomes and patient safety. The Intraoperative Complication Assessment and Reporting with Universal Standards (ICARUS) Global Surgical Collaboration initiated a global effort to address these shortcomings, and the establishment of an adverse event data collection system is an essential step. In this study, we present the core-set variables for collecting iAEs that were based on the globally validated ICARUS criteria for surgical/interventional and anesthesiologic intraoperative adverse event collection and reporting.Material and Methods: This article includes three tools to capture the essential aspects of iAEs. The core-set variables were developed from the globally validated ICARUS criteria for reporting iAEs (item 1). Next, the summary table was developed to guide researchers in summarizing the accumulated iAE data in item 1 (item 2). Finally, this article includes examples of the method and results sections to include in a manuscript reporting iAE data (item 3). Then, 5 scenarios demonstrating best practices for completing items 1-3 were presented both in prose and in a video produced by the ICARUS collaboration.Dissemination: This article provides the surgical community with the tools for collecting essential iAE data. The ICARUS collaboration has already published the 13 criteria for reporting surgical adverse events, but this article is unique and essential as it actually provides the tools for iAE collection. The study team plans to collect feedback for future directions of adverse event collection and reporting.

You have accessJournal of UrologyCME1 Apr 2023PD41-12 PERIOPERATIVE COMPLICATIONS AND OUTCOMES OF PATIENTS UNDERGOING AQUABLATION FOR BENIGN PROSTATIC HYPERPLASIA: A SINGLE TERTIARY REFERRAL CENTER EXPERIENCE IN 146 PATIENTS Alireza Ghoreifi, David Ortega Herrera, Michael Eppler, Randall Lee, Maria Lizana, Abhisek Venkat, Marissa Maas, Andre Abreu, Rene Sotelo, Mike Nguyen, Inderbir Gill, Leo Doumanian, Giovanni E. Cacciamani, and Mihir Desai Alireza GhoreifiAlireza Ghoreifi More articles by this author , David Ortega HerreraDavid Ortega Herrera More articles by this author , Michael EpplerMichael Eppler More articles by this author , Randall LeeRandall Lee More articles by this author , Maria LizanaMaria Lizana More articles by this author , Abhisek VenkatAbhisek Venkat More articles by this author , Marissa MaasMarissa Maas More articles by this author , Andre AbreuAndre Abreu More articles by this author , Rene SoteloRene Sotelo More articles by this author , Mike NguyenMike Nguyen More articles by this author , Inderbir GillInderbir Gill More articles by this author , Leo DoumanianLeo Doumanian More articles by this author , Giovanni E. CacciamaniGiovanni E. Cacciamani More articles by this author , and Mihir DesaiMihir Desai More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003346.12AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Most published data with Aquablation, a recently introduced minimally-invasive technology for management of patients with benign prostatic hyperplasia (BPH), is from 2 large multicenter trials. The aim of this study is to report post-commercialization perioperative complications and outcomes of Aquablation from a single institution. METHODS: Using our IRB-approved database, we retrospectively reviewed the records of consecutive patients who underwent Aquablation for BPH in our institution between August 2020 and June 2022. Those with no available 90-day data were excluded. Primary and secondary outcomes were 90-day complications (graded by Clavien-Dindo classification) and 90-day readmission, respectively. Specific focus was placed on hemorrhagic complications. Univariate and multivariable logistic regression were performed to assess the factors affecting the 90-day complications. RESULTS: Among 146 patients who received Aquablation during the study timeframe, 133 patients with a median (IQR) age of 69 (64 – 73) years and median (IQR) prostate size of 85 (65 – 109) mL were included in the analysis. Baseline and clinical features of the patients are presented in Table 1. Median operative time was 64 minutes, and no intraoperative complication/blood transfusion was recorded. Median length of hospital stay and catheter time were 1 and 3 days, respectively. 90-day complications were recorded in 36 patients (27%) with a Clavien 3 in 11 patients (8%). A bleeding event was recorded in 5 patients (4%) of whom 4 required cystoscopic fulguration (Table 2). Average drop in postoperative hemoglobin was 1.5 gm/dL, yet no patient required peri-operative blood transfusions. The readmission rate was 4.5% (6/133). Two patients underwent re-treatment (transurethral resection of the prostate). On multivariable analysis, prostate size was not independently associated with 90-day complications (OR 1.02, 95% CI 0.99 – 1.03, p=0.07). CONCLUSIONS: Aquablation is safe with a 27% overall complication rate that is independent of patient or prostate factors. Bleeding rate with incorporation of selective cautery hemostasis has dropped compared to the Water and Water 2 trial data. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e1063 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Alireza Ghoreifi More articles by this author David Ortega Herrera More articles by this author Michael Eppler More articles by this author Randall Lee More articles by this author Maria Lizana More articles by this author Abhisek Venkat More articles by this author Marissa Maas More articles by this author Andre Abreu More articles by this author Rene Sotelo More articles by this author Mike Nguyen More articles by this author Inderbir Gill More articles by this author Leo Doumanian More articles by this author Giovanni E. Cacciamani More articles by this author Mihir Desai More articles by this author Expand All Advertisement PDF downloadLoading ...