APA (7th ed.) Citation

J, S., J, A., A, G., S, G., RA, H., J, M., . . . A, S. (2026). Machine learning analysis of continuous glucose monitoring identifies a novel dysglycemic phenotype found in most people with cystic fibrosis. Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society, 25(3), 474. https://doi.org/10.1016/j.jcf.2026.04.001

Chicago Style (17th ed.) Citation

J, Song, Alvarez J, Gent A, Gillespie S, Harris RA, McNeany J, Daley T, Kamaleswaran R, and Stecenko A. "Machine Learning Analysis of Continuous Glucose Monitoring Identifies a Novel Dysglycemic Phenotype Found in Most People with Cystic Fibrosis." Journal of Cystic Fibrosis : Official Journal of the European Cystic Fibrosis Society 25, no. 3 (2026): 474. https://doi.org/10.1016/j.jcf.2026.04.001.

MLA (9th ed.) Citation

J, Song, et al. "Machine Learning Analysis of Continuous Glucose Monitoring Identifies a Novel Dysglycemic Phenotype Found in Most People with Cystic Fibrosis." Journal of Cystic Fibrosis : Official Journal of the European Cystic Fibrosis Society, vol. 25, no. 3, 2026, p. 474, https://doi.org/10.1016/j.jcf.2026.04.001.