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.

Generative artificial intelligence in surgery.

Generative artificial intelligence is able to collect, extract, digest, and generate information in an understandable way for humans. As the first surgical applications of generative artificial intelligence are applied, this perspective paper aims to provide a comprehensive overview of current applications and future perspectives for the application of generative artificial intelligence in surgery, from preoperative planning to training. Generative artificial intelligence can be used before surgery for planning and decision support by extracting patient information and providing patients with information and simulation regarding the procedure. Intraoperatively, generative artificial intelligence can document data that is normally not captured as intraoperative adverse events or provide information to help decision-making. Postoperatively, GAIs can help with patient discharge and follow-up. The ability to provide real-time feedback and store it for later review is an important capability of GAIs. GAI applications are emerging as highly specialized, task-specific tools for tasks such as data extraction, synthesis, presentation, and communication within the realm of surgery. GAIs have the potential to play a pivotal role in facilitating interaction between surgeons and artificial intelligence.

Open versus Robot-Assisted Radical Cystectomy for the Treatment of pT4a Bladder Cancer: Comparison of Perioperative Outcomes.

We compared the perioperative outcomes of open (ORC) vs. robot-assisted (RARC) radical cystectomy in the treatment of pT4a MIBC. In total, 212 patients underwent ORC (102 patients, Group A) vs. RARC (110 patients, Group B) for pT4a bladder cancer. Patients were prospectively followed and retrospectively reviewed. We assessed operative time, estimated blood loss (EBL), intraoperative and postoperative complications, length of stay, transfusion rate, and oncological outcomes. Preoperative features were comparable. The mean operative time was 232.8 vs. 189.2 min (p = 0.04), and mean EBL was 832.8 vs. 523.7 mL in Group A vs. B (p = 0.04). An intraoperative transfusion was performed in 32 (31.4%) vs. 11 (10.0%) cases during ORC vs. RARC (p = 0.03). The intraoperative complications rate was comparable. The mean length of stay was shorter after RARC (12.6 vs. 7.2 days, p = 0.02). Postoperative transfusions were performed in 36 (35.3%) vs. 13 (11.8%) cases (p = 0.03), and postoperative complications occurred in 37 (36.3%) vs. 29 (26.4%) patients in Groups A vs. B (p = 0.05). The positive surgical margin (PSM) rate was lower after RARC. No differences were recorded according to the oncological outcomes. ORC and RARC are feasible treatments for the management of pT4a bladder tumors. Minimally invasive surgery provides shorter operative time, bleeding, transfusion rate, postoperative complications, length of stay, and PSM rate.

A nomogram to predict the absence of clinically significant prostate cancer in males with negative MRI

PURPOSE: To create a nomogram to predict the absence of clinically significant prostate cancer (CSPCa) in males with non-suspicion multiparametric magnetic resonance imaging (mpMRI) undergoing prostate biopsy (PBx). MATERIALS AND METHODS: We identified consecutive patients who underwent 3T mpMRI followed by PBx for suspicion of PCa or surveillance follow-up. All patients had Prostate Imaging Reporting and Data System score 1-2 (negative mpMRI). CSPCa was defined as Grade Group ≥2. Multivariate logistic regression analysis was performed via backward elimination. Discrimination was evaluated with area under the receiver operating characteristic (AUROC). Internal validation with 1,000x bootstrapping for estimating the optimism corrected AUROC. RESULTS: Total 327 patients met inclusion criteria. The median (IQR) age and PSA density (PSAD) were 64 years (58-70) and 0.10 ng/mL2 (0.07-0.15), respectively. Biopsy history was as follows: 117 (36%) males were PBx-naive, 130 (40%) had previous negative PBx and 80 (24%) had previous positive PBx. The majority were White (65%); 6% of males self-reported Black. Overall, 44 (13%) patients were diagnosed with CSPCa on PBx. Black race, history of previous negative PBx and PSAD ≥0.15ng/mL2 were independent predictors for CSPCa on PBx and were included in the nomogram. The AUROC of the nomogram was 0.78 and the optimism corrected AUROC was 0.75. CONCLUSIONS: Our nomogram facilitates evaluating individual probability of CSPCa on PBx in males with PIRADS 1-2 mpMRI and may be used to identify those in whom PBx may be safely avoided. Black males have increased risk of CSPCa on PBx, even in the setting of PIRADS 1-2 mpMRI.

Framework for a living systematic review and meta-analysis for the surgical treatment of bladder cancer: introducing EVIglance to urology.

Background: Knowledge of current and ongoing studies is critical for identifying research gaps and enabling evidence-based decisions for individualized treatment. However, the increasing number of scientific publications poses challenges for healthcare providers and patients in all medical fields to stay updated with the latest evidence. To overcome these barriers, we aim to develop a living systematic review and open-access online evidence map of surgical therapy for bladder cancer (BC), including meta-analyses. Methods: Following the guidelines provided in the Cochrane Handbook for Systematic Reviews of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement, a systematic literature search on uro-oncological therapy in BC will be performed across various literature databases. Within the scope of a meta-analysis and living systematic review, relevant randomized controlled trials will be identified. Data extraction and quantitative analysis will be conducted, along with a critical appraisal of the quality and risk of bias of each study. The available research evidence will be entered into an open-access framework (www.evidencemap.surgery) and will also be accessible via the EVIglance app. Regular semi-automatic updates will enable the implementation of a real-living review concept and facilitate resource-efficient screening. Discussion: A regularly updated evidence map provides professionals and patients with an open-access knowledge base on the current state of research, allowing for decision-making based on recent evidence. It will help identify an oversupply of evidence, thus avoiding redundant work. Furthermore, by identifying research gaps, new hypotheses can be formulated more precisely, enabling planning, determination of sample size, and definition of endpoints for future trials.

Patients' Trust in Artificial Intelligence-based Decision-making for Localized Prostate Cancer: Results from a Prospective Trial.

BACKGROUND: Artificial intelligence (AI) has the potential to enhance diagnostic accuracy and improve treatment outcomes. However, AI integration into clinical workflows and patient perspectives remain unclear. OBJECTIVE: To determine patients' trust in AI and their perception of urologists relying on AI, and future diagnostic and therapeutic AI applications for patients. DESIGN, SETTING, AND PARTICIPANTS: A prospective trial was conducted involving patients who received diagnostic or therapeutic interventions for prostate cancer (PC). INTERVENTION: Patients were asked to complete a survey before magnetic resonance imaging, prostate biopsy, or radical prostatectomy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary outcome was patient trust in AI. Secondary outcomes were the choice of AI in treatment settings and traits attributed to AI and urologists. RESULTS AND LIMITATIONS: Data for 466 patients were analyzed. The cumulative affinity for technology was positively correlated with trust in AI (correlation coefficient 0.094; p = 0.04), whereas patient age, level of education, and subjective perception of illness were not (p > 0.05). The mean score (± standard deviation) for trust in capability was higher for physicians than for AI for responding in an individualized way when communicating a diagnosis (4.51 ± 0.76 vs 3.38 ± 1.07; mean difference [MD] 1.130, 95% confidence interval [CI] 1.010-1.250; t924 = 18.52, p < 0.001; Cohen's d = 1.040) and for explaining information in an understandable way (4.57 ± vs 3.18 ± 1.09; MD 1.392, 95% CI 1.275-1.509; t921 = 27.27, p < 0.001; Cohen's d = 1.216). Patients stated that they had higher trust in a diagnosis made by AI controlled by a physician versus AI not controlled by a physician (4.31 ± 0.88 vs 1.75 ± 0.93; MD 2.561, 95% CI 2.444-2.678; t925 = 42.89, p < 0.001; Cohen's d = 2.818). AI-assisted physicians (66.74%) were preferred over physicians alone (29.61%), physicians controlled by AI (2.36%), and AI alone (0.64%) for treatment in the current clinical scenario. CONCLUSIONS: Trust in future diagnostic and therapeutic AI-based treatment relies on optimal integration with urologists as the human-machine interface to leverage human and AI capabilities. PATIENT SUMMARY: Artificial intelligence (AI) will play a role in diagnostic decisions in prostate cancer in the future. At present, patients prefer AI-assisted urologists over urologists alone, AI alone, and AI-controlled urologists. Specific traits of AI and urologists could be used to optimize diagnosis and treatment for patients with prostate cancer.

Transperineal vs transrectal magnetic resonance and ultrasound image fusion prostate biopsy: a pair-matched comparison.

The objective of this study was to compare transperineal (TP) versus transrectal (TR) magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) fusion prostate biopsy (PBx). Consecutive men who underwent prostate MRI followed by a systematic biopsy. Additional target biopsies were performed from Prostate Imaging Reporting & Data System (PIRADS) 3-5 lesions. Men who underwent TP PBx were matched 1:2 with a synchronous cohort undergoing TR PBx by PSA, Prostate volume (PV) and PIRADS score. Endpoint of the study was the detection of clinically significant prostate cancer (CSPCa; Grade Group ≥ 2). Univariate and multivariable analyses were performed. Results were considered statistically significant if p < 0.05. Overall, 504 patients met the inclusion criteria. A total of 168 TP PBx were pair-matched to 336 TR PBx patients. Baseline demographics and imaging characteristics were similar between the groups. Per patient, the CSPCa detection was 2.1% vs 6.3% (p = 0.4) for PIRADS 1-2, and 59% vs 60% (p = 0.9) for PIRADS 3-5, on TP vs TR PBx, respectively. Per lesion, the CSPCa detection for PIRADS 3 (21% vs 16%; p = 0.4), PIRADS 4 (51% vs 44%; p = 0.8) and PIRADS 5 (76% vs 84%; p = 0.3) was similar for TP vs TR PBx, respectively. However, the TP PBx showed a longer maximum cancer core length (11 vs 9 mm; p = 0.02) and higher cancer core involvement (83% vs 65%; p < 0.001) than TR PBx. Independent predictors for CSPCa detection were age, PSA, PV, abnormal digital rectal examination findings, and PIRADS 3-5. Our study demonstrated transperineal MRI/TRUS fusion PBx provides similar CSPCa detection, with larger prostate cancer core length and percent of core involvement, than transrectal PBx.

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.

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.

A systematic review and meta-analysis of artificial intelligence diagnostic accuracy in prostate cancer histology identification and grading.

BACKGROUND: Artificial intelligence (AI) is a promising tool in pathology, including cancer diagnosis, subtyping, grading, and prognostic prediction. METHODS: The aim of the study is to assess AI application in prostate cancer (PCa) histology. We carried out a systematic literature search in 3 databases. Primary outcome was AI accuracy in differentiating between PCa and benign hyperplasia. Secondary outcomes were AI accuracy in determining Gleason grade and agreement among AI and pathologists. RESULTS: Our final sample consists of 24 studies conducted from 2007 to 2021. They aggregate data from roughly 8000 cases of prostate biopsy and 458 cases of radical prostatectomy (RP). Sensitivity for PCa diagnostic exceeded 90% and ranged from 87% to 100%, and specificity varied from 68% to 99%. Overall accuracy ranged from 83.7% to 98.3% with AUC reaching 0.99. The meta-analysis using the Mantel-Haenszel method showed pooled sensitivity of 0.96 with I2 = 80.7% and pooled specificity of 0.95 with I2 = 86.1%. Pooled positive likehood ratio was 15.3 with I2 = 87.3% and negative - was 0.04 with I2 = 78.6%. SROC (symmetric receiver operating characteristics) curve represents AUC = 0.99. For grading the accuracy of AI was lower: sensitivity for Gleason grading ranged from 77% to 87%, and specificity from 82% to 90%. CONCLUSIONS: The accuracy of AI for PCa identification and grading is comparable to expert pathologists. This is a promising approach which has several possible clinical applications resulting in expedite and optimize pathology reports. AI introduction into common practice may be limited by difficult and time-consuming convolutional neural network training and tuning.

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.

Impact of smoking on urologic cancers: a snapshot of current evidence.

PURPOSE: The purpose of this paper is to present evidence regarding the associations between smoking and the following urologic cancers: prostate, bladder, renal, and upper tract urothelial cancer (UTUC). METHODS: This is a narrative review. PubMed was queried for evidence-based analyses and trials regarding the associations between smoking and prostate, bladder, renal, and UTUC tumors from inception to September 1, 2022. Emphasis was placed on articles referenced in national guidelines and protocols. RESULTS: Prostate-multiple studies associate smoking with higher Gleason score, higher tumor stage, and extracapsular invasion. Though smoking has not yet been linked to tumorigenesis, there is evidence that it plays a role in biochemical recurrence and cancer-specific mortality. Bladder-smoking is strongly associated with bladder cancer, likely due to DNA damage from the release of carcinogenic compounds. Additionally, smoking has been linked to increased cancer-specific mortality and higher risk of tumor recurrence. Renal-smoking tobacco has been associated with tumorigenesis, higher tumor grade and stage, poorer mortality rates, and a greater risk of tumor recurrence. UTUC-tumorigenesis has been associated with smoking tobacco. Additionally, more advanced disease, higher stage, lymph node metastases, poorer survival outcomes, and tumor recurrence have been linked to smoking. CONCLUSION: Smoking has been shown to significantly affect most urologic cancers and has been associated with more aggressive disease, poorer outcomes, and tumor recurrence. The role of smoking cessation is still unclear, but appears to provide some protective effect. Urologists have an opportunity to engage in primary prevention by encouraging cessation practices.

Comparing Oncological and Perioperative Outcomes of Open versus Laparoscopic versus Robotic Radical Nephroureterectomy for the Treatment of Upper Tract Urothelial Carcinoma: A Multicenter, Multinational, Propensity Score-Matched Analysis.

OBJECTIVES: To identify correlates of survival and perioperative outcomes of upper tract urothelial carcinoma (UTUC) patients undergoing open (ORNU), laparoscopic (LRNU), and robotic (RRNU) radical nephroureterectomy (RNU). METHODS: We conducted a retrospective, multicenter study that included non-metastatic UTUC patients who underwent RNU between 1990-2020. Multiple imputation by chained equations was used to impute missing data. Patients were divided into three groups based on their surgical treatment and were adjusted by 1:1:1 propensity score matching (PSM). Survival outcomes per group were estimated for recurrence-free survival (RFS), bladder recurrence-free survival (BRFS), cancer-specific survival (CSS), and overall survival (OS). Perioperative outcomes: Intraoperative blood loss, hospital length of stay (LOS), and overall (OPC) and major postoperative complications (MPCs; defined as Clavien-Dindo > 3) were assessed between groups. RESULTS: Of the 2434 patients included, 756 remained after PSM with 252 in each group. The three groups had similar baseline clinicopathological characteristics. The median follow-up was 32 months. Kaplan-Meier and log-rank tests demonstrated similar RFS, CSS, and OS between groups. BRFS was found to be superior with ORNU. Using multivariable regression analyses, LRNU and RRNU were independently associated with worse BRFS (HR 1.66, 95% CI 1.22-2.28, p = 0.001 and HR 1.73, 95%CI 1.22-2.47, p = 0.002, respectively). LRNU and RRNU were associated with a significantly shorter LOS (beta -1.1, 95% CI -2.2-0.02, p = 0.047 and beta -6.1, 95% CI -7.2-5.0, p < 0.001, respectively) and fewer MPCs (OR 0.5, 95% CI 0.31-0.79, p = 0.003 and OR 0.27, 95% CI 0.16-0.46, p < 0.001, respectively). CONCLUSIONS: In this large international cohort, we demonstrated similar RFS, CSS, and OS among ORNU, LRNU, and RRNU. However, LRNU and RRNU were associated with significantly worse BRFS, but a shorter LOS and fewer MPCs.

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.