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Objectives: The purpose of this study is to evaluate the validity of the standard approach in expert judgment for evaluating precision medicines, in which experts are required to estimate outcomes as if they did not have access to diagnostic information, whereas in fact, they do.
Methods: Fourteen clinicians participated in an expert judgment task to estimate the cost and medical outcomes of the use of exome sequencing in pediatric patients with intractable epilepsy in Thailand. Experts were randomly assigned to either an "unblind" or "blind" group; the former was provided with the exome sequencing results for each patient case prior to the judgment task, whereas the latter was not provided with the exome sequencing results. Both groups were asked to estimate the outcomes for the counterfactual scenario, in which patients had not been tested by exome sequencing.
Results: Our study did not show significant results, possibly due to the small sample size of both participants and case studies.
Conclusions: A comparison of the unblind and blind approach did not show conclusive evidence that there is a difference in outcomes. However, until further evidence suggests otherwise, we recommend the blind approach as preferable when using expert judgment to evaluate precision medicines because this approach is more representative of the counterfactual scenario than the unblind approach.
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http://dx.doi.org/10.1017/S0266462323002714 | DOI Listing |
J Prof Nurs
September 2025
College of Nursing, Brigham Young University, 566 KMBL, Provo, UT 84602, United States of America.
Background: Formal mentoring within the nursing profession has been recognized as an effective approach in teaching critical thinking, leadership skills, communication, and professional socialization. Unfortunately, few baccalaureate nursing programs teach skills specific to mentoring, both as mentees and mentors within a formalized program.
Methods: A peer mentoring program with senior students mentoring sophomore students was developed based on Benner's (1984) novice-to-expert theory during the COVID-19 pandemic.
Risk Anal
September 2025
Edward J. Bloustein School, Rutgers University, New Brunswick, New Jersey, USA.
This AI-assisted review article offers a dual review: a book review of Living with Risk in the Late Roman World by Cam Grey, and a critical review of the current potential of large language models (LLMs), specifically ChatGPT's DeepResearch mode, to assist in thoughtful and scholarly book reviewing within risk science. Grey's book presents an innovative reconstruction of how communities in the late Roman Empire perceived and adapted to chronic environmental and societal risks, emphasizing spatial variability, cultural interpretation, and the normalization of uncertainty. Drawing on commentary from a human reviewer and a parallel AI-assisted analysis, we compare the distinct strengths and limitations of each approach.
View Article and Find Full Text PDFFront Sports Act Living
August 2025
School of Human Sciences (Exercise and Sports Science), The University of Western Australia, Perth, WA, Australia.
Introduction: This study explores the potential of artificial intelligence (AI) to enhance the accuracy and efficiency of video review systems in Taekwondo, addressing limitations in current human-based judgment processes during competitions.
Methods: A total of 241 video review cases from the 2024 Paris Olympic Taekwondo competition were analyzed. AI-based judgments were generated using ChatGPT-4.
Front Oncol
August 2025
Department of Ophthalmology, The First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory for the Prevention and Treatment of Major Blinding Eye Diseases, Chongqing, China.
Background: Uveal melanoma is the most common primary intraocular malignancy in adults, yet radiotherapy decision-making for this disease often remains complex and variable. Although emerging generative AI models have shown promise in synthesizing vast clinical information, few studies have systematically compared their performance against experienced radiation oncologists in this specialized domain. This study examined the comparative accuracy of three leading generative AI models and experienced radiation oncologists in guideline-based clinical decision-making for uveal melanoma.
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