Literature DB >> 25627261

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

BACKGROUND: Prediction models are developed to aid healthcare 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.
METHODS: 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, healthcare 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.
RESULTS: The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study.
CONCLUSION: 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. A complete checklist is available at http://www.tripod-statement.org.
© 2015 American College of Physicians.

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Year:  2015        PMID: 25627261     DOI: 10.1002/bjs.9736

Source DB:  PubMed          Journal:  Br J Surg        ISSN: 0007-1323            Impact factor:   6.939


  170 in total

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3.  Critical appraisal of predictive tools to assess the difficulty of laparoscopic liver resection: a systematic review.

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4.  Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults.

Authors:  Jaime Lynn Speiser; Kathryn E Callahan; Denise K Houston; Jason Fanning; Thomas M Gill; Jack M Guralnik; Anne B Newman; Marco Pahor; W Jack Rejeski; Michael E Miller
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5.  Modeling preoperative risk factors for potentially lethal morbidities using a nationwide Japanese web-based database of patients undergoing distal gastrectomy for gastric cancer.

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6.  Assessment of transparency indicators across the biomedical literature: How open is open?

Authors:  Stylianos Serghiou; Despina G Contopoulos-Ioannidis; Kevin W Boyack; Nico Riedel; Joshua D Wallach; John P A Ioannidis
Journal:  PLoS Biol       Date:  2021-03-01       Impact factor: 8.029

7.  Probabilistic forecasting of surgical case duration using machine learning: model development and validation.

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Review 8.  Tools for predicting patient-reported outcomes in prostate cancer patients undergoing radical prostatectomy: a systematic review of prognostic accuracy and validity.

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Journal:  Prostate Cancer Prostatic Dis       Date:  2017-06-06       Impact factor: 5.554

9.  Bracing in Adolescent Idiopathic Scoliosis Trial (BrAIST): Development and Validation of a Prognostic Model in Untreated Adolescent Idiopathic Scoliosis Using the Simplified Skeletal Maturity System.

Authors:  Lori A Dolan; Stuart L Weinstein; Mark F Abel; Patrick P Bosch; Matthew B Dobbs; Tyler O Farber; Matthew F Halsey; M Timothy Hresko; Walter F Krengel; Charles T Mehlman; James O Sanders; Richard M Schwend; Suken A Shah; Kushagra Verma
Journal:  Spine Deform       Date:  2019-11

10.  Development of Multivariable Models to Predict and Benchmark Transfusion in Elective Surgery Supporting Patient Blood Management.

Authors:  Dieter Hayn; Karl Kreiner; Hubert Ebner; Peter Kastner; Nada Breznik; Angelika Rzepka; Axel Hofmann; Hans Gombotz; Günter Schreier
Journal:  Appl Clin Inform       Date:  2017-06-14       Impact factor: 2.342

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