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| 荟萃分析诊断准确性研究× | 诊断准确性研究设计× | |
|---|---|---|
| 领域≠ | 流行病学 | 临床研究 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1993–2005 (foundational models) | 2003-2015 |
| 提出者≠ | Moses, Shapiro & Littenberg (SROC framework, 1993); Reitsma et al. (bivariate model, 2005) | Bossuyt, Reitsma, and STARD group (2003); clinical epidemiology pioneers |
| 类型≠ | Quantitative systematic synthesis | Research Design |
| 开创性文献≠ | Reitsma, J. B., Glas, A. S., Rutjes, A. W., Scholten, R. J., Bossuyt, P. M., & Zwinderman, A. H. (2005). Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. Journal of Clinical Epidemiology, 58(10), 982–990. DOI ↗ | Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., ... & de Vet, H. C. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Annals of Internal Medicine, 138(1), 40–44. DOI ↗ |
| 别名 | DTA meta-analysis, diagnostic meta-analysis, systematic review of diagnostic accuracy, pooled diagnostic accuracy | diagnostic accuracy study, test accuracy, STARD, diagnostic evaluation |
| 相关 | 2 | 2 |
| 摘要≠ | A meta-analytic diagnostic accuracy study systematically identifies and pools sensitivity and specificity data from multiple primary diagnostic test accuracy studies. Using the bivariate or hierarchical summary ROC (HSROC) model, it produces a joint summary of a test's ability to correctly classify diseased and non-diseased individuals across diverse clinical settings, accounting for the inherent trade-off between sensitivity and specificity. | A diagnostic accuracy study evaluates how well a new diagnostic test (or biomarker, imaging modality, clinical assessment) detects the presence or absence of disease compared to a reference standard (gold standard). Standardized since 2003 by the STARD (Standards for Reporting of Diagnostic Accuracy Studies) initiative, diagnostic accuracy studies are fundamental to clinical medicine, determining whether and how new tests can improve patient diagnosis and treatment. |
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