{"id":"W2975460762","doi":"10.1177/1073110519876166","title":"Cochrane's Linked Data Project: How it Can Advance our Understanding of Surrogate Endpoints","year":2019,"lang":"en","type":"article","venue":"The Journal of Law Medicine & Ethics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cochrane","funders":"","keywords":"Surrogate endpoint; Data science; Computer science; Real world evidence; Medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.177968,0.002142336,0.003898874,0.04263104,0.003524713,0.02033935,0.005862745,0.01106804,0.03998325],"category_scores_gemma":[0.57186,0.002862092,0.007646438,0.04801295,0.004895288,0.01603179,0.01914955,0.009283784,0.008996368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008840867,"about_ca_system_score_gemma":0.06800215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04840533,"about_ca_topic_score_gemma":0.03013691,"domain_scores_codex":[0.8474099,0.09944753,0.02521548,0.005748417,0.02036947,0.00180916],"domain_scores_gemma":[0.3265794,0.5443191,0.01957049,0.06401379,0.03622207,0.009295179],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001269989,0.0002149002,0.003149304,0.02050207,0.002060053,0.0003616962,0.002400978,0.004599845,0.000484296,0.2829555,0.4587191,0.2232822],"study_design_scores_gemma":[0.0007915754,0.00008959042,0.001382554,0.01948227,0.001093562,0.0002116028,0.0003664175,0.0026191,0.0008807894,0.1233208,0.8494946,0.000267233],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003678023,0.05766506,0.3275729,0.2032121,0.00647032,0.007878451,0.2724769,0.02394656,0.09709977],"genre_scores_gemma":[0.02082236,0.0391297,0.7733926,0.02087939,0.002011754,0.0131507,0.1111625,0.01102963,0.008421397],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.822032,"threshold_uncertainty_score":0.9411962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2374418428644161,"score_gpt":0.4257170681773276,"score_spread":0.1882752253129116,"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."}}