Objectives To investigate the diagnostic performance of the coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) for detecting COVID-19. Methods We searched PubMed, EMBASE, MEDLINE, Web of Science, Cochrane Library, and Scopus database until September 21, 2021. Statistical analysis included data pooling, forest plot construction, heterogeneity testing, meta-regression, and subgroup analyses. Results We included 24 studies with 8382 patients. The pooled sensitivity and specificity and the area under the curve (AUC) of CO-RADS ≥ 3 for detecting COVID-19 were 0.89 (95% confidence interval (CI) 0.85–0.93), 0.68 (95% CI 0.60–0.75), and 0.87 (95% CI 0.84–0.90), respectively. The pooled sensitivity and specificity and AUC of CO-RADS ≥ 4 were 0.83 (95% CI 0.79–0.87), 0.84 (95% CI 0.78–0.88), and 0.90 (95% CI 0.87–0.92), respectively. Cochran’s Q test ( p < 0.01) and Higgins I 2 heterogeneity index revealed considerable heterogeneity. Studies with both symptomatic and asymptomatic patients had higher specificity than those with only symptomatic patients using CO-RADS ≥ 3 and CO-RADS ≥ 4. Using CO-RADS ≥ 4, studies with participants aged < 60 years had higher sensitivity (0.88 vs. 0.80, p = 0.02) and lower specificity (0.77 vs. 0.87, p = 0.01) than studies with participants aged > 60 years. Conclusions CO-RADS has favorable performance in detecting COVID-19. CO-RADS ≥ 3/4 might be applied as cutoff values given their high sensitivity and specificity. However, there is a need for more well-designed studies on CO-RADS. Key Points • CO-RADS shows a favorable performance in detecting COVID-19. • CO-RADS ≥ 3 had a high sensitivity 0.89 (95% CI 0.85–0.93), and it may prove advantageous in screening the potentially infected people to prevent the spread of COVID-19. • CO-RADS ≥ 4 had high specificity 0.84 (95% CI 0.78–0.88) and may be more suitable for definite diagnosis of COVID-19.

The train timetable and station operation plan play a critical role in the high‐speed railway (HSR) planning and management. The existing hierarchical optimization methods for the planning process of the HSR would affect the efficiency of train schedules and are often difficult to achieve an optimized scheme. This paper proposes a position‐track‐time three‐dimensional network, which describes the process of train operations in sections and stations at a macroscopic scale, while the track infrastructure including the position of insulation joints in stations are modeled microscopically. The modeled train running times and dwell times are based on standard timetable design values given in full minutes by the China Railway Corporation, while the interlocking times and minimum headway times are not specified explicitly. The problem is formulated as a large‐scale 0–1 linear integer programming model, which is solved using an extended branch‐and‐price algorithm. The effectiveness and precision of the model are verified through a real‐world case study on the Beijing–Shanghai HSR line. The results indicate that the proposed model can effectively improve the line capacity by 17.2% while ensuring that there is no conflict between train operations in sections and stations.