J
Jan Y Verbakel
Researcher at University of Oxford
Publications - 150
Citations - 6106
Jan Y Verbakel is an academic researcher from University of Oxford. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 22, co-authored 105 publications receiving 3558 citations. Previous affiliations of Jan Y Verbakel include Katholieke Universiteit Leuven & National Institute for Health Research.
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Journal ArticleDOI
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
Laure Wynants,Laure Wynants,Ben Van Calster,Ben Van Calster,Gary S. Collins,Gary S. Collins,Richard D Riley,Georg Heinze,Ewoud Schuit,Marc J.M. Bonten,Darren Dahly,Johanna A A G Damen,Thomas P. A. Debray,Valentijn M.T. de Jong,Maarten De Vos,Paula Dhiman,Paula Dhiman,Maria C Haller,Michael O. Harhay,Liesbet Henckaerts,Pauline Heus,Michael Kammer,Nina Kreuzberger,Anna Lohmann,Kim Luijken,Jie Ma,Glen P. Martin,David J. McLernon,Constanza L Andaur Navarro,Johannes B. Reitsma,Jamie C. Sergeant,Chunhu Shi,Nicole Skoetz,Luc J.M. Smits,Kym I E Snell,Matthew Sperrin,René Spijker,René Spijker,Ewout W. Steyerberg,Toshihiko Takada,Ioanna Tzoulaki,Ioanna Tzoulaki,Sander M. J. van Kuijk,Bas C T van Bussel,Bas C T van Bussel,Iwan C. C. van der Horst,Florien S. van Royen,Jan Y Verbakel,Jan Y Verbakel,Christine Wallisch,Christine Wallisch,Jack Wilkinson,Robert Wolff,Lotty Hooft,Karel G.M. Moons,Maarten van Smeden +55 more
TL;DR: Proposed models for covid-19 are poorly reported, at high risk of bias, and their reported performance is probably optimistic, according to a review of published and preprint reports.
Journal ArticleDOI
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
Evangelia Christodoulou,Jie Ma,Gary S. Collins,Ewout W. Steyerberg,Jan Y Verbakel,Ben Van Calster,Ben Van Calster +6 more
TL;DR: Improvements in methodology and reporting are needed for studies that compare modeling algorithms for clinical prediction modeling in the literature and found no evidence of superior performance of ML over LR.
Journal ArticleDOI
Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators.
Ben Van Calster,Ben Van Calster,Laure Wynants,Jan F.M. Verbeek,Jan Y Verbakel,Jan Y Verbakel,Evangelia Christodoulou,Andrew J. Vickers,Monique J. Roobol,Ewout W. Steyerberg,Ewout W. Steyerberg +10 more
TL;DR: Recommendations on interpreting and reporting DCA when evaluating prediction models are provided to help researchers understand DCA and improve application and reporting.
Journal ArticleDOI
Systematic review and validation of prediction rules for identifying children with serious infections in emergency departments and urgent-access primary care
Matthew Thompson,A Van den Bruel,Jan Y Verbakel,Monica Lakhanpaul,T Haj-Hassan,Richard Stevens,Henriëtte A. Moll,Frank Buntinx,Marjolein Y. Berger,Bert Aertgeerts,Rianne Oostenbrink,David Mant +11 more
TL;DR: Several clinical features are useful to increase or decrease the probability that a child has a serious infection, but none is sufficient on its own to substantially raise or lower the risk of serious infection.
Final report for HTA Project 07/37/05: Systematic review and validation of prediction rules for identifying children with serious infections in emergency departments and urgent-access primary care
Matthew Thompson,A Van den Bruel,Jan Y Verbakel,Monica Lakhanpaul,T Haj-Hassan,R Srevens,F Moll,Frank Buntinx,Marjolein Y. Berger,Bert Aertgeerts,Rianne Oostenbrink,David Mant +11 more
TL;DR: The most useful clinical features for ruling in serious infection was parental or clinician overall concern that the illness was different from previous illnesses or that something was wrong as mentioned in this paper, and the best performing clinical prediction rule was a five-stage decision tree rule, consisting of the physician's gut feeling, dyspnoea, temperature ≥ 40 °C, diarrhoea and age.