{"id":"W2739093696","doi":"","title":"VALUE Expert Meeting(Tokyo)ハイリスク高血圧患者の大規模臨床試験；VALUE Studyから何を学ぶか？","year":2005,"lang":"ja","type":"article","venue":"Pharma Medica","topic":"Pharmacy and Medical Practices","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Value (mathematics); Computer science; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.008792129,0.001706312,0.001803637,0.0005381163,0.001726639,0.0001848605,0.00275728,0.00162311,0.05507394],"category_scores_gemma":[0.003347871,0.001657384,0.0007956977,0.00106816,0.002003086,0.001595601,0.001050696,0.008187467,0.009666882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006446873,"about_ca_system_score_gemma":0.001251648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001869492,"about_ca_topic_score_gemma":0.00001874164,"domain_scores_codex":[0.9838342,0.005368855,0.002653594,0.002149905,0.002536946,0.003456468],"domain_scores_gemma":[0.9876987,0.005091738,0.001328972,0.001101003,0.0003508188,0.004428771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002603372,0.007728809,0.0005642144,0.0005313141,0.003442678,0.001334276,0.01506091,0.0007629169,0.04270187,0.001098746,0.6455311,0.2786398],"study_design_scores_gemma":[0.01204021,0.0003956973,0.0001229481,0.0003102301,0.002103792,0.0003345681,0.001130909,0.02794207,0.07250186,0.0001639787,0.8814139,0.001539771],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1585119,0.2194902,0.0003255599,0.3062991,0.04853441,0.005122629,0.0007202445,0.001878309,0.2591177],"genre_scores_gemma":[0.7812944,0.06366043,0.001186028,0.1258766,0.01952726,0.0003645399,0.0001395569,0.0002551718,0.007696046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6227825,"threshold_uncertainty_score":0.999673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1459158345230181,"score_gpt":0.4896647148004869,"score_spread":0.3437488802774689,"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."}}