These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
262 related articles for article (PubMed ID: 32028825)
1. Examining Bias and Reporting in Oral Health Prediction Modeling Studies. Du M; Haag D; Song Y; Lynch J; Mittinty M J Dent Res; 2020 Apr; 99(4):374-387. PubMed ID: 32028825 [TBL] [Abstract][Full Text] [Related]
2. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. Collins GS; Dhiman P; Andaur Navarro CL; Ma J; Hooft L; Reitsma JB; Logullo P; Beam AL; Peng L; Van Calster B; van Smeden M; Riley RD; Moons KG BMJ Open; 2021 Jul; 11(7):e048008. PubMed ID: 34244270 [TBL] [Abstract][Full Text] [Related]
3. Bias and Reporting Quality of Clinical Prognostic Models for Idiopathic Pulmonary Fibrosis: A Cross-Sectional Study. Di J; Li X; Yang J; Li L; Yu X Risk Manag Healthc Policy; 2022; 15():1189-1201. PubMed ID: 35702399 [TBL] [Abstract][Full Text] [Related]
4. Prediction models for skin tears in the elderly: A systematic review and meta-analysis. Fan S; Jiang H; Shen J; Lin H; Yang L; Yu D; Zhang M; Zheng N; Chen L Geriatr Nurs; 2024; 59():103-112. PubMed ID: 38996767 [TBL] [Abstract][Full Text] [Related]
5. Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement. Heus P; Damen JAAG; Pajouheshnia R; Scholten RJPM; Reitsma JB; Collins GS; Altman DG; Moons KGM; Hooft L BMC Med; 2018 Jul; 16(1):120. PubMed ID: 30021577 [TBL] [Abstract][Full Text] [Related]
6. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. Nagendran M; Chen Y; Lovejoy CA; Gordon AC; Komorowski M; Harvey H; Topol EJ; Ioannidis JPA; Collins GS; Maruthappu M BMJ; 2020 Mar; 368():m689. PubMed ID: 32213531 [TBL] [Abstract][Full Text] [Related]
7. Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review. Yang Q; Fan X; Cao X; Hao W; Lu J; Wei J; Tian J; Yin M; Ge L Acta Obstet Gynecol Scand; 2023 Jan; 102(1):7-14. PubMed ID: 36397723 [TBL] [Abstract][Full Text] [Related]
8. Risk Prediction Models for Cardiotoxicity of Chemotherapy Among Patients With Breast Cancer: A Systematic Review. Kaboré EG; Macdonald C; Kaboré A; Didier R; Arveux P; Meda N; Boutron-Ruault MC; Guenancia C JAMA Netw Open; 2023 Feb; 6(2):e230569. PubMed ID: 36821108 [TBL] [Abstract][Full Text] [Related]
9. Assessment of bias in scoring of AI-based radiotherapy segmentation and planning studies using modified TRIPOD and PROBAST guidelines as an example. Hurkmans C; Bibault JE; Clementel E; Dhont J; van Elmpt W; Kantidakis G; Andratschke N Radiother Oncol; 2024 May; 194():110196. PubMed ID: 38432311 [TBL] [Abstract][Full Text] [Related]
10. Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques. Andaur Navarro CL; Damen JAAG; Takada T; Nijman SWJ; Dhiman P; Ma J; Collins GS; Bajpai R; Riley RD; Moons KG; Hooft L BMJ Open; 2020 Nov; 10(11):e038832. PubMed ID: 33177137 [TBL] [Abstract][Full Text] [Related]
11. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement. Collins GS; Reitsma JB; Altman DG; Moons KGM; Eur Urol; 2015 Jun; 67(6):1142-1151. PubMed ID: 25572824 [TBL] [Abstract][Full Text] [Related]
12. Multivariable prediction models for health care spending using machine learning: a protocol of a systematic review. Huang AW; Haslberger M; Coulibaly N; Galárraga O; Oganisian A; Belbasis L; Panagiotou OA Diagn Progn Res; 2022 Mar; 6(1):4. PubMed ID: 35321760 [TBL] [Abstract][Full Text] [Related]
13. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Collins GS; Reitsma JB; Altman DG; Moons KG Br J Surg; 2015 Feb; 102(3):148-58. PubMed ID: 25627261 [TBL] [Abstract][Full Text] [Related]
14. Reporting and Methods in Developing Prognostic Prediction Models for Metabolic Syndrome: A Systematic Review and Critical Appraisal. Zhang H; Shao J; Chen D; Zou P; Cui N; Tang L; Wang D; Ye Z Diabetes Metab Syndr Obes; 2020; 13():4981-4992. PubMed ID: 33364802 [TBL] [Abstract][Full Text] [Related]
15. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Collins GS; Reitsma JB; Altman DG; Moons KG Br J Cancer; 2015 Jan; 112(2):251-9. PubMed ID: 25562432 [TBL] [Abstract][Full Text] [Related]
16. Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review. Munguía-Realpozo P; Etchegaray-Morales I; Mendoza-Pinto C; Méndez-Martínez S; Osorio-Peña ÁD; Ayón-Aguilar J; García-Carrasco M Autoimmun Rev; 2023 May; 22(5):103294. PubMed ID: 36791873 [TBL] [Abstract][Full Text] [Related]
17. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. The TRIPOD Group. Collins GS; Reitsma JB; Altman DG; Moons KG; Circulation; 2015 Jan; 131(2):211-9. PubMed ID: 25561516 [TBL] [Abstract][Full Text] [Related]
18. Systematic review of machine-learning models in orthopaedic trauma. Dijkstra H; van de Kuit A; de Groot T; Canta O; Groot OQ; Oosterhoff JH; Doornberg JN; ; ; van den Bekerom M; Calderon SL; Colaris J; Duis KT; Esfahani SA; DiGiovanni C; Gordon M; Guss D; IJpma F; Jaarsma R; Janssen M; Jayakumar P; Kerkhoffs GM; Leighton R; van Munster B; Poolman R; Ring D; Schemtisch E; Stirler V; Tornetta P; Wijffels M Bone Jt Open; 2024 Jan; 5(1):9-19. PubMed ID: 38226447 [TBL] [Abstract][Full Text] [Related]