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Comparison of Overall Survival Between Preoperative Chemotherapy and Chemoradiotherapy for Resectable Pancreatic Adenocarcinoma

Ali A. Mokdad, Rebecca M. Minter, Adam C. Yopp, Matthew R. Porembka, Sam C. Wang, Hong Zhu, Mathew M. Augustine, John C. Mansour, Michael A. Choti, and Patricio M. Polanco

Background: Preoperative therapy is being increasingly used in the treatment of resectable pancreatic cancer. Because there are only limited data on the optimal preoperative regimen, we compared overall survival (OS) between preoperative chemotherapy (CT) and preoperative chemoradiotherapy (CRT) in resectable pancreatic adenocarcinoma. Patients and Methods: Patients receiving preoperative therapy and resection for clinical T1–3N0–1M0 adenocarcinoma of the pancreas were identified in the National Cancer Database for 2006 through 2012. We constructed inverse probability of treatment weights to balance baseline group differences, and compared OS between CT and CRT, as well as pathologic and postoperative findings. Results: We identified 1,326 patients (CT: 616; CRT: 710). Differences in OS were not significant between CRT and CT (median survival, 25 vs 26 months; P=.10; weight-adjusted hazard ratio, 0.89; 95% CI, 0.77–1.02). Compared with patients in the CT group, those in the CRT group had lower pathologic T stage (ypT0/T1/T2: 36% vs 21%; P<.01), less lymph node involvement (ypN1: 35% vs 59%; P<.01), and fewer positive resection margins (14% vs 21%; P=.01), but had more postoperative unplanned readmissions (9% vs 6%; P=.01) and increased 90-day mortality (7% vs 4%; P=.03). Those in the CRT group were also less likely to receive postoperative therapy (26% vs 51%; P<.01). Conclusions: Preoperative CT and CRT have similar OS, but CRT is associated with more favorable pathologic features at the cost of higher postoperative morbidity and mortality. Additional trials investigating preoperative therapy are needed for patients with resectable pancreatic cancer.

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Enhancing Readability of Online Patient-Facing Content: The Role of AI Chatbots in Improving Cancer Information Accessibility

Andres A. Abreu, Gilbert Z. Murimwa, Emile Farah, James W. Stewart II, Lucia Zhang, Jonathan Rodriguez, John Sweetenham, Herbert J. Zeh III, Sam C. Wang, and Patricio M. Polanco

Background: Internet-based health education is increasingly vital in patient care. However, the readability of online information often exceeds the average reading level of the US population, limiting accessibility and comprehension. This study investigates the use of chatbot artificial intelligence to improve the readability of cancer-related patient-facing content. Methods: We used ChatGPT 4.0 to rewrite content about breast, colon, lung, prostate, and pancreas cancer across 34 websites associated with NCCN Member Institutions. Readability was analyzed using Fry Readability Score, Flesch-Kincaid Grade Level, Gunning Fog Index, and Simple Measure of Gobbledygook. The primary outcome was the mean readability score for the original and artificial intelligence (AI)–generated content. As secondary outcomes, we assessed the accuracy, similarity, and quality using F1 scores, cosine similarity scores, and section 2 of the DISCERN instrument, respectively. Results: The mean readability level across the 34 websites was equivalent to a university freshman level (grade 13±1.5). However, after ChatGPT’s intervention, the AI-generated outputs had a mean readability score equivalent to a high school freshman education level (grade 9±0.8). The overall F1 score for the rewritten content was 0.87, the precision score was 0.934, and the recall score was 0.814. Compared with their original counterparts, the AI-rewritten content had a cosine similarity score of 0.915 (95% CI, 0.908–0.922). The improved readability was attributed to simpler words and shorter sentences. The mean DISCERN score of the random sample of AI-generated content was equivalent to “good” (28.5±5), with no significant differences compared with their original counterparts. Conclusions: Our study demonstrates the potential of AI chatbots to improve the readability of patient-facing content while maintaining content quality. The decrease in requisite literacy after AI revision emphasizes the potential of this technology to reduce health care disparities caused by a mismatch between educational resources available to a patient and their health literacy.