Abstract：
METHODS: A systemic review and a meta-analysis. We also provided a retrospective cohort for validation in this study.
OBJECTIVE: (1) Using a meta-analysis to determine the pooled discriminatory ability of The Skeletal Oncology Research Group (SORG) classical algorithm (CA) and machine learning algorithms (MLA); and (2) test the hypothesis that SORG-CA has less variability in performance than SORG-MLA in non-American validation cohorts as SORG-CA does not incorporates regional-specific variables such as body mass index as input.
METHODS: After data extraction from the included studies, logit-transformation was applied for extracted AUCs for further analysis. The discriminatory abilities of both algorithms were directly compared by their logit (AUC)s. Further subgroup analysis by region (America vs non-America) was also conducted by comparing the corresponding logit (AUC).
RESULTS: The pooled logit (AUC)s of 90-day SORG-CA was .82 (95% confidence interval [CI], .53-.11), 1-year SORG-CA was 1.11 (95% CI, .74-1.48), 90-day SORG-MLA was 1.36 (95% CI, 1.09-1.63), and 1-year SORG-MLA was 1.57 (95% CI, 1.17-1.98). All the algorithms performed better in United States than in Taiwan (P < .001). The performance of SORG-CA was less influenced by a non-American cohort than SORG-MLA.
CONCLUSIONS: These observations might highlight the importance of incorporating region-specific variables into existing models to make them generalizable to racially or geographically distinct regions.
OBJECTIVE: (1) Using a meta-analysis to determine the pooled discriminatory ability of The Skeletal Oncology Research Group (SORG) classical algorithm (CA) and machine learning algorithms (MLA); and (2) test the hypothesis that SORG-CA has less variability in performance than SORG-MLA in non-American validation cohorts as SORG-CA does not incorporates regional-specific variables such as body mass index as input.
METHODS: After data extraction from the included studies, logit-transformation was applied for extracted AUCs for further analysis. The discriminatory abilities of both algorithms were directly compared by their logit (AUC)s. Further subgroup analysis by region (America vs non-America) was also conducted by comparing the corresponding logit (AUC).
RESULTS: The pooled logit (AUC)s of 90-day SORG-CA was .82 (95% confidence interval [CI], .53-.11), 1-year SORG-CA was 1.11 (95% CI, .74-1.48), 90-day SORG-MLA was 1.36 (95% CI, 1.09-1.63), and 1-year SORG-MLA was 1.57 (95% CI, 1.17-1.98). All the algorithms performed better in United States than in Taiwan (P < .001). The performance of SORG-CA was less influenced by a non-American cohort than SORG-MLA.
CONCLUSIONS: These observations might highlight the importance of incorporating region-specific variables into existing models to make them generalizable to racially or geographically distinct regions.
摘要：
方法：系统综述和荟萃分析。我们还提供了一个回顾性队列用于本研究的验证。
目的：（1）使用荟萃分析来确定骨骼肿瘤学研究小组（SORG）经典算法（CA）和机器学习算法（MLA）的合并辨别能力；（2）检验以下假设：在非美国验证队列中，SORG-CA的性能变异性小于SORG-MLA，因为SORG-MLA不包含特定的体重指数，例如CA输入。
方法：从纳入的研究中提取数据后，将logit-transformation应用于提取的AUC以进行进一步分析。通过两种算法的logit（AUC）s直接比较了两种算法的判别能力。通过比较相应的logit（AUC），还进行了按地区（美国与非美国）的进一步亚组分析。
结果：90天SORG-CA的合并logit（AUC）为.82（95％置信区间[CI]，.53-.11)，1年期SORG-CA为1.11（95%CI，0.74-1.48），90天SORG-MLA为1.36（95%CI，1.09-1.63），1年期SORG-MLA为1.57（95%CI，1.17-1.98）。所有算法在美国的表现都优于台湾(P<.001)。与SORG-MLA相比，SORG-CA的性能受非美国队列的影响较小。
结论：这些观察结果可能突出了将特定地区变量纳入现有模型的重要性，使其可推广到种族或地理上不同的地区。
目的：（1）使用荟萃分析来确定骨骼肿瘤学研究小组（SORG）经典算法（CA）和机器学习算法（MLA）的合并辨别能力；（2）检验以下假设：在非美国验证队列中，SORG-CA的性能变异性小于SORG-MLA，因为SORG-MLA不包含特定的体重指数，例如CA输入。
方法：从纳入的研究中提取数据后，将logit-transformation应用于提取的AUC以进行进一步分析。通过两种算法的logit（AUC）s直接比较了两种算法的判别能力。通过比较相应的logit（AUC），还进行了按地区（美国与非美国）的进一步亚组分析。
结果：90天SORG-CA的合并logit（AUC）为.82（95％置信区间[CI]，.53-.11)，1年期SORG-CA为1.11（95%CI，0.74-1.48），90天SORG-MLA为1.36（95%CI，1.09-1.63），1年期SORG-MLA为1.57（95%CI，1.17-1.98）。所有算法在美国的表现都优于台湾(P<.001)。与SORG-MLA相比，SORG-CA的性能受非美国队列的影响较小。
结论：这些观察结果可能突出了将特定地区变量纳入现有模型的重要性，使其可推广到种族或地理上不同的地区。
