Complications and adverse events in lymphadenectomy of the inguinal area: worldwide expert consensus.

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.

Accuracy, readability, and understandability of large language models for prostate cancer information to the public.

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.

Study protocol for the Intraoperative Complications Assessment and Reporting with Universal Standards (ICARUS) global cross-specialty surveys and consensus.

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.

Publishers' and journals' instructions to authors on use of generative artificial intelligence in academic and scientific publishing: bibliometric analysis.

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.

Awareness and Use of ChatGPT and Large Language Models: A Prospective Cross-sectional Global Survey in Urology.

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.

Bridging the Gap Between Urological Research and Patient Understanding: The Role of Large Language Models in Automated Generation of Layperson's Summaries.

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.

Discharge prescription patterns for antiplatelet and statin therapy following carotid endarterectomy: an analysis of the vascular quality initiative.

OBJECTIVES: Despite guidelines endorsing statin and single antiplatelet therapy (SAPT) therapy post-carotid endarterectomy (CEA), these medications may be either under or inappropriately prescribed. We determined rates of new statin prescriptions as well as change in antiplatelet therapy (APT) regimen at discharge. We identified characteristics associated with these occurrences. DESIGN: We performed a retrospective Vascular Quality Initiative registry analysis of more than 125 000 patients who underwent CEA from 2013 to 2021. SETTING: The Vascular Quality Initiative is a multicentre registry database including academic and community-based hospitals throughout the USA. PARTICIPANTS: Patients age≥18 years undergoing CEA with available statin and APT data (preprocedure and postprocedure) were included. PRIMARY AND SECONDARY OUTCOME MEASURES: We determined overall rates of statin and APT prescription at discharge. Multivariate logistic regression was used to determine clinical and demographic characteristics that were mostly associated with new statin prescription or changes in APT regimen at discharge. RESULTS: Study participants were predominantly male (61%) and White (90%), with a mean age of 70.6±9.1. 13.1% of participants were not on statin therapy pre-CEA, and 48% of these individuals were newly prescribed one. Statin rates steadily increased throughout the study period: 36.2% in 2013 to 62% in 2021. A higher likelihood of new statin prescription was associated with non-race, diabetes, coronary heart disease, stroke, TIA and a non-elective indication. Older age, female gender, chronic obstructive pulmonary disease and prior carotid revascularisation were associated with a lower likelihood of new statin prescription. Nearly all participants were discharged on APT (63% SAPT and 37% dual antiplatelet therapy, DAPT). Among these individuals, 16% were discharged on a regimen that was different from the one on admission (11 947 (10.7%) of patients were upgraded to DAPT and 5813 (5.2%) were downgraded to SAPT). CONCLUSIONS: Although statin use has substantially improved following CEA, more than half of individuals not on a statin preprocedure remained this way at discharge. In addition, DAPT at discharge was frequent, a quarter of whom were on SAPT preprocedure. Further efforts are needed to improve rates of new statin prescriptions, ensure appropriate APT intensity at discharge and determine how different discharge APT regimens impact outcomes.

Evaluating the Effectiveness of Artificial Intelligence-powered Large Language Models Application in Disseminating Appropriate and Readable Health Information in Urology.

PURPOSE: The Internet is a ubiquitous source of medical information, and natural language processors are gaining popularity as alternatives to traditional search engines. However, suitability of their generated content for patients is not well understood. We aimed to evaluate the appropriateness and readability of natural language processor-generated responses to urology-related medical inquiries. MATERIALS AND METHODS: Eighteen patient questions were developed based on Google Trends and were used as inputs in ChatGPT. Three categories were assessed: oncologic, benign, and emergency. Questions in each category were either treatment or sign/symptom-related questions. Three native English-speaking Board-Certified urologists independently assessed appropriateness of ChatGPT outputs for patient counseling using accuracy, comprehensiveness, and clarity as proxies for appropriateness. Readability was assessed using the Flesch Reading Ease and Flesh-Kincaid Reading Grade Level formulas. Additional measures were created based on validated tools and assessed by 3 independent reviewers. RESULTS: Fourteen of 18 (77.8%) responses were deemed appropriate, with clarity having the most 4 and 5 scores (P = .01). There was no significant difference in appropriateness of the responses between treatments and symptoms or between different categories of conditions. The most common reason from urologists for low scores was responses lacking information-sometimes vital information. The mean (SD) Flesch Reading Ease score was 35.5 (SD=10.2) and the mean Flesh-Kincaid Reading Grade Level score was 13.5 (1.74). Additional quality assessment scores showed no significant differences between different categories of conditions. CONCLUSIONS: Despite impressive capabilities, natural language processors have limitations as sources of medical information. Refinement is crucial before adoption for this purpose.

Clinical Patient Summaries Not Fit for Purpose: A Study in Urology.

We evaluated the comprehensibility of patient summaries provided by urology journals for the general public. The WebFX online tool was used to assess the readability of abstracts and patient summaries by scoring the text according to established readability indices. A total of 266 articles were included and statistical analysis was performed to compare the readability of abstracts and patient summaries, stratified by article type and text type. The results show that patient summaries consistently performed worse than abstracts for all readability metrics, and the readability levels for both abstracts and patient summaries were more advanced than the recommended guidelines on average. This study suggests that patient summaries provided by these urology journals may not be easily understood by the general population, and tools should be developed to help urological researchers improve the accessibility of their work. PATIENT SUMMARY: We checked how easy it is to read and understand patient summaries and abstracts of research articles from four urology journals. We found that the summaries and abstracts were too hard to read. This study shows that we need to make these summaries easier to read for everyone.

Bibliometric analysis of academic journal recommendations and requirements for surgical and anesthesiologic adverse events reporting.

BACKGROUND: Standards for reporting surgical adverse events (AEs) vary widely within the scientific literature. Failure to adequately capture AEs hinders efforts to measure the safety of healthcare delivery and improve the quality of care. The aim of the present study is to assess the prevalence and typology of perioperative AE reporting guidelines among surgery and anesthesiology journals. MATERIALS AND METHODS: In November 2021, three independent reviewers queried journal lists from the SCImago Journal & Country Rank (SJR) portal (www.scimagojr.com), a bibliometric indicator database for surgery and anesthesiology academic journals. Journal characteristics were summarized using SCImago, a bibliometric indicator database extracted from Scopus journal data. Quartile 1 (Q1) was considered the top quartile and Q4 bottom quartile based on the journal impact factor. Journal author guidelines were collected to determine whether AE reporting recommendations were included and, if so, the preferred reporting procedures. RESULTS: Of 1409 journals queried, 655 (46.5%) recommended surgical AE reporting. Journals most likely to recommend AE reporting were: by category surgery (59.1%), urology (53.3%), and anesthesia (52.3%); in top SJR quartiles (i.e. more influential); by region, based in Western Europe (49.8%), North America (49.3%), and the Middle East (48.3%). CONCLUSIONS: Surgery and anesthesiology journals do not consistently require or provide recommendations on perioperative AE reporting. Journal guidelines regarding AE reporting should be standardized and are needed to improve the quality of surgical AE reporting with the ultimate goal of improving patient morbidity and mortality.

Severity Grading Systems for Intraoperative Adverse Events. A Systematic Review of the Literature and Citation Analysis.

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.

Robotic-assisted Simple Prostatectomy: An Intrafascial Approach for a Prostate of 470 g.

BACKGROUND: Simple prostatectomy has been established as a management option for lower urinary tract symptoms (LUTS) due to benign prostatic hyperplasia (BPH) with glands larger than 80 cc.1 Robotic-assisted simple prostatectomy (RASP) has been established as a safe and effective management option. This has been reported in multiple studies comparing it with the open approach where no statistically significant differences in complication rates have been found.2 The intrafascial approach is intended to add multiple benefits, such as not requiring continuous bladder irrigation and resection of a larger percentage of the adenoma, potentially leading to a lower reoperation rate and diagnosis of prostate cancer in patients in which this was missed preoperatively.3 OBJECTIVE: To present the resection of a very large prostate can lead to significant intraoperative challenges. Therefore, we demosntrate our intrafascial robotic management of a 470g prostate. MATERIALS AND METHODS: A 56-year-old with LUTS (IPSS: 34) for over 6 years, complicated with multiple episodes of urinary retention. PSA: 20.6 ng/mL. MRI reported a 463 cc prostate volume without focal lesions. First, the retropubic space was developed. Cystotomy was performed at the anterior bladder neck. A plane between the prostatic capsule and the surrounding fascia was dissected while carefully preserving the neurovascular bundles. Due to its large size, the prostate was divided into segments to facilitate its extraction. Hemostatic control and vesicourethral anastomosis were performed. RESULTS: Discharged on postoperative day 1 with a Jackson-Pratt drain and Foley catheter removed on days 6 and 9, respectively. Pathology was negative for malignancy. Patient-reported improvement of LUTS (IPSS: 3) and preservation of erectile function (SHIM: 23) at the 3-month follow-up. CONCLUSION: The intrafascial technique can be useful in cases where a large-sized prostate hinders a transvesical approach. Additionally, it allows for an easier piecemeal of the gland. A larger series is needed to determine its potential benefits.

Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis.

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.

Recommendations for Intraoperative Adverse Events Data Collection in Clinical Studies and Study Protocols. An ICARUS Global Surgical Collaboration Study.

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. Highlights: This article represents a novel, fully-encompassing system for the data collection of intraoperative adverse events.The presented core-set variables for reporting intraoperative adverse events are not based solely on our opinion, but rather are synthesized from the globally validated ICARUS criteria for reporting intraoperative adverse events.Together, the included text, figures, and ICARUS collaboration-produced video should equip any surgeon, anesthesiologist, or nurse with the tools to properly collect intraoperative adverse event data.Future directions include translation of this article to allow for the widest possible adoption of this important collection system.