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Träfflista för sökning "WFRF:(Bjorndal A) srt2:(2020-2023)"

Search: WFRF:(Bjorndal A) > (2020-2023)

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1.
  • Ruggeri, Kai, et al. (author)
  • The globalizability of temporal discounting
  • 2022
  • In: Nature Human Behaviour. - : Springer Nature. - 2397-3374. ; 6:10, s. 1386-1397
  • Journal article (peer-reviewed)abstract
    • Economic inequality is associated with preferences for smaller, immediate gains over larger, delayed ones. Such temporal discounting may feed into rising global inequality, yet it is unclear whether it is a function of choice preferences or norms, or rather the absence of sufficient resources for immediate needs. It is also not clear whether these reflect true differences in choice patterns between income groups. We tested temporal discounting and five intertemporal choice anomalies using local currencies and value standards in 61 countries (N = 13,629). Across a diverse sample, we found consistent, robust rates of choice anomalies. Lower-income groups were not significantly different, but economic inequality and broader financial circumstances were clearly correlated with population choice patterns. Ruggeri et al. find in a study of 61 countries that temporal discounting patterns are globally generalizable. Worse financial environments, greater inequality and high inflation are associated with extreme or inconsistent long-term decisions.
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2.
  • Ramezanzade, S., et al. (author)
  • The efficiency of artificial intelligence methods for finding radiographic features in different endodontic treatments-a systematic review
  • 2023
  • In: Acta Odontologica Scandinavica. - 0001-6357. ; 81:6, s. 422-435
  • Journal article (peer-reviewed)abstract
    • ObjectivesTo assess the efficiency of AI methods in finding radiographic features in Endodontic treatment considerations.Material and methodsThis review was based on the PRISMA guidelines and QUADAS 2 tool. A systematic search was performed of the literature on cases with endodontic treatments, comparing AI algorithms (test) versus conventional image assessments (control) for finding radiographic features . The search was conducted in PubMed, Scopus, Google Scholar and the Cochrane library. Inclusion criteria were studies on the use of AI and machine learning in endodontic treatments using dental X-rays.ResultsThe initial search retrieved 1131 papers, from which 24 were included. High heterogeneity of the materials left out a meta-analysis.The reported subcategories were periapical lesion, vertical root fractures, predicting root/canal morphology, locating minor apical foramen, tooth segmentation and endodontic retreatment prediction. Radiographic features assessed were mostly periapical lesions. The studies mostly considered the decision of 1-3 experts as the reference for training their models. Almost half of the included materials campared their trained neural network model with other methods. More than 58% of studies had some level of bias.ConclusionsAI-based models have shown effectiveness in finding radiographic features in different endodontic treatments. While the reported accuracy measurements seem promising, the papers mostly were biased methodologically.
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