APA (7th ed.) Citation

TH, L., HY, C., MJ, J., CK, C., YW, L., CL, P., . . . HS, S. (2026). Nationwide longitudinal evaluation of a machine learning approach for enhanced interpretation of Xpert MTB/RIF ultra rifampicin-resistance results in low bacterial load tuberculosis specimens. Journal of infection and public health, 19(2), 103064. https://doi.org/10.1016/j.jiph.2025.103064

Chicago Style (17th ed.) Citation

TH, Lin, Chung HY, Jian MJ, Chang CK, Lai YW, Perng CL, Chang FY, Chen YH, and Shang HS. "Nationwide Longitudinal Evaluation of a Machine Learning Approach for Enhanced Interpretation of Xpert MTB/RIF Ultra Rifampicin-resistance Results in Low Bacterial Load Tuberculosis Specimens." Journal of Infection and Public Health 19, no. 2 (2026): 103064. https://doi.org/10.1016/j.jiph.2025.103064.

MLA (9th ed.) Citation

TH, Lin, et al. "Nationwide Longitudinal Evaluation of a Machine Learning Approach for Enhanced Interpretation of Xpert MTB/RIF Ultra Rifampicin-resistance Results in Low Bacterial Load Tuberculosis Specimens." Journal of Infection and Public Health, vol. 19, no. 2, 2026, p. 103064, https://doi.org/10.1016/j.jiph.2025.103064.