{"id":"W3164031246","doi":"10.2217/pme-2021-0030","title":"Sino-European Science and Technology Collaboration on Personalized Medicine: Overview, Trends and Future Perspectives","year":2021,"lang":"en","type":"review","venue":"Personalized Medicine","topic":"Science, Research, and Medicine","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 Health; Institute of Genetics; Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; European Commission","keywords":"Personalized medicine; China; Science policy; Health care; Business; Political science; Healthcare system; Public relations; Engineering ethics; Medicine; Knowledge management; Engineering; Public administration; Computer science; Bioinformatics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008042267,0.0008424317,0.001613239,0.006382808,0.0007499597,0.003604204,0.0009864446,0.002266687,0.005502379],"category_scores_gemma":[0.008918259,0.0002843604,0.001355173,0.01041355,0.001000813,0.003626382,0.002341017,0.002147903,0.0009058089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002527758,"about_ca_system_score_gemma":0.01339971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00346653,"about_ca_topic_score_gemma":0.006429655,"domain_scores_codex":[0.997058,0.001121292,0.0005750193,0.0003282159,0.0006217602,0.0002957517],"domain_scores_gemma":[0.9882345,0.007935084,0.001352689,0.0002119896,0.001773303,0.0004923995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000153846,0.00009225159,0.002546132,0.09876115,0.0008212965,0.0003113716,0.0007476375,0.0004391518,0.0004338257,0.03348862,0.0260405,0.8361641],"study_design_scores_gemma":[0.0000424933,0.0001260379,0.004970224,0.1214016,0.0012775,0.001067413,0.001065215,0.0001944017,0.0003706902,0.008665872,0.8607772,0.00004130124],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001844089,0.9963062,0.0001195884,0.002171419,0.0001854841,0.000006619992,0.00003093095,0.000004180615,0.0009911652],"genre_scores_gemma":[0.002874441,0.9957395,0.000251159,0.0007437926,0.0001842137,0.00001575158,0.0000479597,0.000002055525,0.0001412057],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.008042267,"threshold_uncertainty_score":0.04253203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06395361676459436,"score_gpt":0.4258752760560552,"score_spread":0.3619216592914609,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}