A systematic review on performance characteristics of FNA of renal lesions: It is time for a standardized classification system.

BACKGROUND: The incidence of renal tumors has steadily increased over the past decade. In this study, the authors performed a systematic review and analysis of the literature on renal fine-needle aspiration (FNA) to determine its performance and explore whether a standardized classification system can be used for reporting renal FNA cytology. METHODS: A systematic search of published articles on renal FNA was conducted. The data on FNA and histologic diagnosis were extracted and categorized, and the risk of malignancy was calculated. Different scenarios were used to estimate FNA performance statistics. RESULTS: Of the 3766 potentially relevant studies, 23 met the inclusion criteria of the study. The 2231 FNA cases included were re-categorized according to the classification system, rendering 142 (6.36%) nondiagnostic, 270 (12.1%) nonneoplastic, 271 (12.14%) benign neoplasm, 65 (2.91%) renal neoplasm with unknown malignant potential, oncocytic type, 25 (1.12%) atypia of undetermined significance, 60 (2.68%) suspicious for malignancy, and 1398 (62.66%) malignant FNA diagnoses. The risk of malignancy in these cases was 65.4%, 18.1%, 16.6%, 16.9%, 60%, 73.3%, and 96.9%, respectively. According to the classification system, the study indicated that the accuracy of renal FNA was between 91% and 95%, the sensitivity was 90.9%-96.7%, and the specificity was 82%-92% in different scenarios. CONCLUSIONS: There is a need for a standardized reporting in renal cytology that will improve the sensitivity and accuracy of renal cytology, reduce the rate of indeterminate diagnoses, and alter the management strategies of renal lesions. Based on the available literature, a new reporting system is proposed, including categories with an associated risk of malignancy.

Leveraging artificial intelligence to summarize abstracts in lay language for increasing research accessibility and transparency.

OBJECTIVE: Returning aggregate study results is an important ethical responsibility to promote trust and inform decision making, but the practice of providing results to a lay audience is not widely adopted. Barriers include significant cost and time required to develop lay summaries and scarce infrastructure necessary for returning them to the public. Our study aims to generate, evaluate, and implement ChatGPT 4 lay summaries of scientific abstracts on a national clinical study recruitment platform, ResearchMatch, to facilitate timely and cost-effective return of study results at scale. MATERIALS AND METHODS: We engineered prompts to summarize abstracts at a literacy level accessible to the public, prioritizing succinctness, clarity, and practical relevance. Researchers and volunteers assessed ChatGPT-generated lay summaries across five dimensions: accuracy, relevance, accessibility, transparency, and harmfulness. We used precision analysis and adaptive random sampling to determine the optimal number of summaries for evaluation, ensuring high statistical precision. RESULTS: ChatGPT achieved 95.9% (95% CI, 92.1-97.9) accuracy and 96.2% (92.4-98.1) relevance across 192 summary sentences from 33 abstracts based on researcher review. 85.3% (69.9-93.6) of 34 volunteers perceived ChatGPT-generated summaries as more accessible and 73.5% (56.9-85.4) more transparent than the original abstract. None of the summaries were deemed harmful. We expanded ResearchMatch's technical infrastructure to automatically generate and display lay summaries for over 750 published studies that resulted from the platform's recruitment mechanism. DISCUSSION AND CONCLUSION: Implementing AI-generated lay summaries on ResearchMatch demonstrates the potential of a scalable framework generalizable to broader platforms for enhancing research accessibility and transparency.

TechQuity is an imperative for health and technology business: Let's work together to achieve it.

Open discussions of social justice and health inequities may be an uncommon focus within information technology science, business, and health care delivery partnerships. However, the COVID-19 pandemic-which disproportionately affected Black, indigenous, and people of color-has reinforced the need to examine and define roles that technology partners should play to lead anti-racism efforts through our work. In our perspective piece, we describe the imperative to prioritize TechQuity-equity and social justice as a technology business strategy-through collaborating in partnerships that focus on eliminating racial and social inequities.

The Use and Misuse of Statistical Methods in Cytopathology Studies: Review of 6 Journals.

BACKGROUND: The misuse of statistical methods in diagnostic accuracy studies has been criticized in many publications. OBJECTIVE: To assess the use and misuse of statistical methods in medical journal articles. METHODS: We reviewed 141 original articles from 6 cytopathology journals published in 2014. RESULTS: In total, 16 articles used no descriptive statistics and 66 articles used no inferential statistics. Also, 82 articles did not report using any tests for diagnostic accuracy. The most commonly adopted statistical measure for diagnostic accuracy was sensitivity, followed by specificity. The most common inferential statistical methods were χ2 testing, the Fisher exact test, and the Cohen kappa coefficient, respectively. Only 78 articles were free of statistical errors. "No statistics were used although statistical methods were required" was the most common type of error, followed by "failure to select proper statistical methods." CONCLUSION: Our assessment of use of statistical methods in this subsection of pathology demonstrated a need for improvement.