Contact

  • Academic
    s3verett@stanford.edu
    University - Student Department: School of Medicine Position: Graduate, Medicine

Additional Info

  • Mail Code: 5705

All Publications

  • Genetic disorders and associated morbidity, mortality, and congenital anomalies in preterm infants born at less than 34 weeks of gestation. BMC pediatrics Everett, S. S., Bomback, M., Banerjee, A., Sahni, R., Wapner, R. J., Tolia, V. N., Clark, R. H., Lyford, A., Hays, T. 2025

    Abstract

    Genetic disorders are recognized as key contributors to morbidity, mortality, and congenital anomalies in term infants. However, the rates of diagnosis and association with morbidity, mortality, and congenital anomalies in preterm infants are poorly characterized. We sought to determine rates of diagnosis of genetic disorders in preterm infants and to define the association of genetic disorders with morbidity, mortality, and congenital anomalies.This was a multicenter observational cohort study conducted in neonatal intensive care units in the Pediatrix Clinical Data Warehouse. Infants born from 23 to 0/7 to 33 and 6/7 weeks of gestation, admitted to 374 U.S. community and academic neonatal intensive care units from 2000 to 2020 were included. Infants transferred after birth or prior to discharge were excluded. We analyzed diagnosis of genetic disorders; predischarge morbidity (including acute kidney injury, bronchopulmonary dysplasia, necrotizing enterocolitis, sepsis, shock, severe retinopathy, and intracranial hemorrhage); mortality; and presence of congenital anomalies.Among 323,770 early preterm infants analyzed, 4,196 (1.3%) were diagnosed with one of twenty genetic disorders. Single gene disorders were identified in 2,250 (0.7%) infants, copy number variants in 88 (0.03%) infants, and aneuploidies in 1,885 (0.6%) infants. Morbidity, mortality, and congenital anomalies occurred in 1,319 (31.4%), 566 (13.5%), and 1,041 (24.8%) infants with genetic disorders compared to 77,957 (24.5%), 15,240 (4.7%), and 9,455 (3.0%) infants without genetic disorders. Common aneuploidies accounted for most of these associations. However, morbidity, mortality, and congenital anomalies were also significantly more common in early preterm infants with single gene disorders and pathogenic copy number variants. We did not detect meaningful differences in diagnostic rates of genetic disorders over the study period.1.3% of early preterm infants were diagnosed with genetic disorders. Genetic disorders were strongly associated with morbidity, mortality, and congenital anomalies. Clinicians should strongly consider genetic evaluation in early preterm infants with morbidity, mortality, or congenital anomalies. Prospective research is needed to determine the true prevalence of genetic disorders in this high-risk population.

    View details for DOI 10.1186/s12887-025-06373-2

    View details for PubMedID 41331421

  • From Tool to Teammate: A Randomized Controlled Trial of Clinician-AI Collaborative Workflows for Diagnosis. medRxiv : the preprint server for health sciences Everett, S. S., Bunning, B. J., Jain, P., Lopez, I., Agarwal, A., Desai, M., Gallo, R., Goh, E., Kadiyala, V. B., Kanjee, Z., Koshy, J. M., Olson, A., Rodman, A., Schulman, K., Strong, E., Chen, J. H., Horvitz, E. 2025

    Abstract

    Early studies of large language models (LLMs) in clinical settings have largely treated artificial intelligence (AI) as a tool rather than an active collaborator. As LLMs now demonstrate expert-level diagnostic performance, the focus shifts from whether AI can offer valuable suggestions to how it can be effectively integrated into physicians' diagnostic workflows. We conducted a randomized controlled trial (n=70 clinicians) to evaluate the value of employing a custom GPT system designed to engage collaboratively with clinicians on diagnostic reasoning challenges. The collaborative design began with independent diagnostic assessments from both the clinician and the AI. These were then combined in an AI-generated synthesis that integrated the two perspectives, highlighting points of agreement and disagreement and offering commentary on each. We evaluated two workflow variants: one where the AI provided an initial opinion (AI-first), and another where it followed the clinician's assessment (AI-second). Clinicians using either collaborative workflow outperformed those using traditional tools, achieving average accuracies of 85% (AI-first) and 82% (AI-second), compared to 75% with traditional resources (p < 0.0004 and p < 0.00001; mean differences = 9.8% and 6.8%; 95% CI = 4.6%-15% and 4.0%-9.6%). Performance did not differ significantly between workflows or from the AI-alone score of 90%. These results underscore the value of collaborative AI systems that complement clinician expertise and foster effective coordination between human and machine reasoning in diagnostic decision-making.

    View details for DOI 10.1101/2025.06.07.25329176

    View details for PubMedID 40502554

  • Staged Hybrid Treatment of a Large Aneurysmal Pulmonary Sequestration With Thoracic Endovascular Aortic Repair Followed by Lobectomy Annals of Thoracic Surgery Short Reports Everett, S. S., Backus, L. M., Lin, Y., Watkins, A. C., Elliott, I. A. 2025

    View details for DOI 10.1016/j.atssr.2025.09.036