The pooled effect of all-cause mortality rate in non-health care workers and health care workers. by Zhuoyang Xia (21354186)

Cochrane Collaboration’s risk of bias tool. by Zhuoyang Xia (21354186)

“…Regarding COVID-19 severity, no significant difference were found for asymptomatic (pooled RR 1.18; 95%CI: 0.81–1.72), mild to moderate (pooled RR 0.99; 95%CI: 0.84–1.17), severe COVID-19 (pooled RR 1.25; 95%CI: 0.92–1.70), hospitalization (pooled RR 0.93; 95%CI: 0.58–1.50) or all-cause mortality (pooled RR 0.60; 95%CI: 0.18–1.95) between BCG and placebo groups. …”

Grade system. by Zhuoyang Xia (21354186)

“…Regarding COVID-19 severity, no significant difference were found for asymptomatic (pooled RR 1.18; 95%CI: 0.81–1.72), mild to moderate (pooled RR 0.99; 95%CI: 0.84–1.17), severe COVID-19 (pooled RR 1.25; 95%CI: 0.92–1.70), hospitalization (pooled RR 0.93; 95%CI: 0.58–1.50) or all-cause mortality (pooled RR 0.60; 95%CI: 0.18–1.95) between BCG and placebo groups. …”

Literature search. by Zhuoyang Xia (21354186)

“…Regarding COVID-19 severity, no significant difference were found for asymptomatic (pooled RR 1.18; 95%CI: 0.81–1.72), mild to moderate (pooled RR 0.99; 95%CI: 0.84–1.17), severe COVID-19 (pooled RR 1.25; 95%CI: 0.92–1.70), hospitalization (pooled RR 0.93; 95%CI: 0.58–1.50) or all-cause mortality (pooled RR 0.60; 95%CI: 0.18–1.95) between BCG and placebo groups. …”

Study characteristics. by Zhuoyang Xia (21354186)

“…Regarding COVID-19 severity, no significant difference were found for asymptomatic (pooled RR 1.18; 95%CI: 0.81–1.72), mild to moderate (pooled RR 0.99; 95%CI: 0.84–1.17), severe COVID-19 (pooled RR 1.25; 95%CI: 0.92–1.70), hospitalization (pooled RR 0.93; 95%CI: 0.58–1.50) or all-cause mortality (pooled RR 0.60; 95%CI: 0.18–1.95) between BCG and placebo groups. …”

Flowchart of literature retrieval. by Zhuoyang Xia (21354186)

“…Regarding COVID-19 severity, no significant difference were found for asymptomatic (pooled RR 1.18; 95%CI: 0.81–1.72), mild to moderate (pooled RR 0.99; 95%CI: 0.84–1.17), severe COVID-19 (pooled RR 1.25; 95%CI: 0.92–1.70), hospitalization (pooled RR 0.93; 95%CI: 0.58–1.50) or all-cause mortality (pooled RR 0.60; 95%CI: 0.18–1.95) between BCG and placebo groups. …”

Data extraction. by Zhuoyang Xia (21354186)

“…Regarding COVID-19 severity, no significant difference were found for asymptomatic (pooled RR 1.18; 95%CI: 0.81–1.72), mild to moderate (pooled RR 0.99; 95%CI: 0.84–1.17), severe COVID-19 (pooled RR 1.25; 95%CI: 0.92–1.70), hospitalization (pooled RR 0.93; 95%CI: 0.58–1.50) or all-cause mortality (pooled RR 0.60; 95%CI: 0.18–1.95) between BCG and placebo groups. …”

Descriptive statistics of variables. by Yanling Li (269030)

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Matched kernel density function diagram. by Yanling Li (269030)

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Matching estimation results of propensity score. by Yanling Li (269030)

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Sample balance test. by Yanling Li (269030)

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Robustness test results. by Yanling Li (269030)

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Heterogeneity test at different educational levels. by Yanling Li (269030)

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Results of baseline regression. by Yanling Li (269030)

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Unmatched kernel density function diagram. by Yanling Li (269030)

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Risk of bias. by Da Huang (1306407)

“…Normally presenting with symptoms such as dyspnea, decreased exercise tolerance, decreased maximal heart rate, and decreased arterial oxygen saturation. …”

Overall risk of bias assessment. by Da Huang (1306407)

“…Normally presenting with symptoms such as dyspnea, decreased exercise tolerance, decreased maximal heart rate, and decreased arterial oxygen saturation. …”

Sampling scenarios used in our experiments. by Anna Beatriz Silva (20989245)

“…<div><p>Premature birth can be defined as birth before 37 weeks of gestation, which is a significant global health issue, being the main cause for neonatal deaths. …”

Hybridsampling triple size models performance. by Anna Beatriz Silva (20989245)

“…<div><p>Premature birth can be defined as birth before 37 weeks of gestation, which is a significant global health issue, being the main cause for neonatal deaths. …”

PCA-CGAN model parameter settings. by Chao Tang (10925)

“…Simultaneously, we designed a two-stage conditional encoding-decoding architecture that builds category-independent feature spaces from early training stages, fundamentally breaking the feature space bias caused by the “Matthew effect” and effectively preventing majority classes from compressing minority class features during generation. …”