Literature DB >> 25623578

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

G S Collins1, J B Reitsma, D G Altman, K G M Moons.   

Abstract

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
© 2015 Royal College of Obstetricians and Gynaecologists.

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Year:  2015        PMID: 25623578     DOI: 10.1111/1471-0528.13244

Source DB:  PubMed          Journal:  BJOG        ISSN: 1470-0328            Impact factor:   6.531


  17 in total

1.  Creation and validation of the acute heart failure risk score: AHFRS.

Authors:  Susana Garcia-Gutierrez; José Maria Quintana; Ane Antón-Ladislao; Maria Soledad Gallardo; Esther Pulido; Irene Rilo; Elena Zubillaga; Miren Morillas; José Juan Onaindia; Nekane Murga; Ricardo Palenzuela; José González Ruiz
Journal:  Intern Emerg Med       Date:  2016-10-11       Impact factor: 3.397

2.  Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma.

Authors:  Yun Bian; Yan Fang Liu; Hui Jiang; Yinghao Meng; Fang Liu; Kai Cao; Hao Zhang; Xu Fang; Jing Li; Jieyu Yu; Xiaochen Feng; Qi Li; Li Wang; Jianping Lu; Chengwei Shao
Journal:  Abdom Radiol (NY)       Date:  2021-06-29

3.  Noncontrast Radiomics Approach for Predicting Grades of Nonfunctional Pancreatic Neuroendocrine Tumors.

Authors:  Yun Bian; Zengrui Zhao; Hui Jiang; Xu Fang; Jing Li; Kai Cao; Chao Ma; Shiwei Guo; Li Wang; Gang Jin; Jianping Lu; Jun Xu
Journal:  J Magn Reson Imaging       Date:  2020-04-28       Impact factor: 4.813

4.  Performance of CT-based radiomics in diagnosis of superior mesenteric vein resection margin in patients with pancreatic head cancer.

Authors:  Yun Bian; Hui Jiang; Chao Ma; Kai Cao; Xu Fang; Jing Li; Li Wang; Jianming Zheng; Jianping Lu
Journal:  Abdom Radiol (NY)       Date:  2020-03

5.  A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients.

Authors:  Francesca Arezzo; Gennaro Cormio; Daniele La Forgia; Carla Mariaflavia Santarsiero; Michele Mongelli; Claudio Lombardi; Gerardo Cazzato; Ettore Cicinelli; Vera Loizzi
Journal:  Arch Gynecol Obstet       Date:  2022-05-09       Impact factor: 2.493

6.  There is a paucity of economic evaluations of prediction methods of caries and periodontitis-A systematic review.

Authors:  Helena Fransson; Thomas Davidson; Madeleine Rohlin; Helena Christell
Journal:  Clin Exp Dent Res       Date:  2021-02-16

7.  Guidelines for Conducting Ethical Artificial Intelligence Research in Neurology: A Systematic Approach for Clinicians and Researchers.

Authors:  Sharon Chiang; Rosalind W Picard; Winston Chiong; Robert Moss; Gregory A Worrell; Vikram R Rao; Daniel M Goldenholz
Journal:  Neurology       Date:  2021-07-27       Impact factor: 11.800

8.  Hypothermia as a predictor for mortality in trauma patients at admittance to the Intensive Care Unit.

Authors:  Kirsten Balvers; Marjolein Van der Horst; Maarten Graumans; Christa Boer; Jan M Binnekade; J Carel Goslings; Nicole P Juffermans
Journal:  J Emerg Trauma Shock       Date:  2016 Jul-Sep

9.  Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2.

Authors:  Xu Fang; Xiao Li; Yun Bian; Xiang Ji; Jianping Lu
Journal:  Eur Radiol       Date:  2020-07-03       Impact factor: 7.034

10.  Development and Validation of Prognostic Nomograms Based on Gross Tumor Volume and Cervical Nodal Volume for Nasopharyngeal Carcinoma Patients With Concurrent Chemoradiotherapy.

Authors:  Cui-Dai Zhang; Mei Li; Ying-Ji Hong; Ze-Man Cai; Kai-Chun Huang; Zhi-Xiong Lin; Zhi-Ning Yang
Journal:  Front Oncol       Date:  2021-06-28       Impact factor: 6.244

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