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.

We sought to determine whether there is a subset of men who can avoid prostate biopsy based on multiparametric magnetic resonance imaging and clinical characteristics. Of 1,149 consecutive men who underwent prostate biopsy from October 2011 to March 2017, 135 had prebiopsy negative multiparametric magnetic resonance imaging with PI-RADS™ (Prostate Imaging Reporting and Data System) score less than 3. The detection rate of clinically significant prostate cancer was evaluated according to prostate specific antigen density and prior biopsy history. Clinically significant prostate cancer was defined as Grade Group 2 or greater. Multivariable logistic regression analysis was performed to identify predictors of nonclinically significant prostate cancer on biopsy. The prostate cancer and clinically significant prostate cancer detection rates were 38% and 18%, respectively. Men with biopsy detected, clinically significant prostate cancer had a smaller prostate (p = 0.004), higher prostate specific antigen density (p = 0.02) and no history of prior negative biopsy (p = 0.01) compared to the nonclinically significant prostate cancer cohort. Prostate specific antigen density less than 0.15 ng/ml/cc (p <0.001) and prior negative biopsy (p = 0.005) were independent predictors of absent clinically significant prostate cancer on biopsy. The negative predictive value of multiparametric magnetic resonance imaging for biopsy detection of clinically significant prostate cancer improved with decreasing prostate specific antigen density, primarily in men with prior negative biopsy (p = 0.001) but not in biopsy naïve men. Of the men 32% had the combination of negative multiparametric magnetic resonance imaging, prostate specific antigen density less than 0.15 ng/ml/cc and negative prior biopsy, and none had clinically significant prostate cancer on repeat biopsy. The incidence of biopsy identified, clinically significant prostate cancer was 18%, 10% and 0% in men with negative multiparametric magnetic resonance imaging only, men with negative multiparametric magnetic resonance imaging and prostate specific antigen density less than 0.15 ng/ml/cc, and men with negative multiparametric magnetic resonance imaging, prostate specific antigen density less than 0.15 ng/ml/cc and negative prior biopsy, respectively. We propose that a subset of men with negative multiparametric magnetic resonance imaging, prostate specific antigen density less than 0.15 ng/ml/cc and prior negative biopsy may safely avoid rebiopsy. Conversely prostate biopsy should be considered in biopsy naïve men regardless of negative multiparametric magnetic resonance imaging, particularly those with prostate specific antigen density greater than 0.15 ng/ml/cc.

Purpose:We evaluated 5-year oncologic and functional outcomes of hemigland cryoablation of localized prostate cancer.Materials and Methods:We reviewed the records of 160 consecutive men who underwe...

Summary: Genomic surveillance has become a useful tool for better understanding virus pathogenicity, origin and spread. Obtaining accurately assembled, complete viral genomes directly from clinical samples is still a challenging. Here, we describe three protocols using a unique primer set designed to recover long reads of SARS-CoV-2 directly from total RNA extracted from clinical samples. This protocol is useful, accessible and adaptable to laboratories with varying resources and access to distinct sequencing methods: Nanopore, Illumina and/or Sanger.