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Title:           APPRAISE-RS: Automated, updated, participatory, and personalized treatment recommender systems based on GRADE methodology
Subject:         Heliyon, 9 (2023) e13074. doi:10.1016/j.heliyon.2023.e13074
Keywords:        Treatment recommender systems,Evidence-based medicine,Meta-analysis,Attention deficit hyperactivity disorder
Author:          Beatriz López
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      Title: APPRAISE-RS: Automated, updated, participatory, and personalized treatment recommender systems based on GRADE methodology
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       Item: 
        Title: 1 Introduction
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       Item: 
        Title: 2 Related work
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       Item: 
        Title: 3 Methodology
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          Title: 3.1 Background to the GRADE methodology
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         Item: 
          Title: 3.2 Clinical studies dataset
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          Title: 3.3 Data gathering
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          Title: 3.4 Study and preference filtering
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          Title: 3.5 Meta-analysis and quality assessment
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          Title: 3.6 Benefit-risk judgement and quality analysis
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          Title: 3.7 Evidence to decision: GRADE heuristics
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        Title: 4 APPRAISE-RS implementation
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          Title: 4.1 Clinical studies
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          Title: 4.2 Patient data
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          Title: 4.3 Experimental set-up
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        Title: 5 Results
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         Item: 
          Title: 5.1 Automated recommendations
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          Title: 5.2 Updated recommendations
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          Title: 5.3 Personalized recommendations
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          Title: 5.4 Participatory recommendations
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          Title: 5.5 Clinicians’ satisfaction
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        Title: 6 Discussion
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          Title: 6.1 Limitations
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         Item: 
          Title: 6.2 Academic and clinical implications
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        Title: 7 Conclusions
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        Title: Funding statement
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       Item: 
        Title: Author contribution statement
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        Title: Declaration of competing interest
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       Item: 
        Title: Acknowledgements
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       Item: 
        Title: References
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    Author: Beatriz López
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  Checksum: 3a11f99d
   Type: CRC32
  Checksum: c5d5fd93160e4ad6b7e5c5ad9c88a8ff
   Type: MD5
  Checksum: c4c55c1ec82e112b1996a3d45ca6d2b40bc6dc46
   Type: SHA-1
  Checksum: bdebf16365088c412c05fc39d88727a593d973ff3f9278b087d73ceca12bb9a1
   Type: SHA-256