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TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Xehetasun bibliografikoak
Egile Nagusiak: Collins, GS, Moons, KGM, Dhiman, P, Riley, RD, Beam, AL, Van Calster, B, Ghassemi, M, Liu, X, Reitsma, JB, van Smeden, M, Boulesteix, A-L, Camaradou, JC, Celi, LA, Denaxas, S, Denniston, AK, Glocker, B, Golub, RM, Harvey, H, Heinze, G, Hoffman, MM, Kengne, AP, Lam, E, Lee, N, Loder, EW, Maier-Hein, L, Mateen, BA, McCradden, MD, Oakden-Rayner, L, Ordish, J, Parnell, R, Rose, S, Singh, K, Wynants, L, Logullo, P
Formatua: Journal article
Hizkuntza:English
Argitaratua: BMJ Publishing Group 2024
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Antzeko izenburuak

  • 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
    nork: Collins, GS, et al.
    Argitaratua: (2021)
  • Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist
    nork: Debray, TPA, et al.
    Argitaratua: (2023)
  • Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration
    nork: Debray, TPA, et al.
    Argitaratua: (2023)
  • 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
    nork: Gary S Collins, et al.
    Argitaratua: (2021-07-01)
  • Transparent reporting of multivariable prediction models in journal and conference abstracts: TRIPOD for abstracts
    nork: Heus, P, et al.
    Argitaratua: (2020)

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