{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006790781,0.0003313917,0.0003336215,0.00101295,0.001100509,0.001460401,0.0006897444,0.001526224,0.04596711],"category_scores_gemma":[0.01364155,0.0004000243,0.000340761,0.001113373,0.000785362,0.0007217777,0.0006987755,0.00164562,0.004920306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001736368,"about_ca_system_score_gemma":0.005242814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005847394,"about_ca_topic_score_gemma":0.01492037,"domain_scores_codex":[0.9965485,0.001184798,0.0003345749,0.0003099548,0.001317683,0.0003045868],"domain_scores_gemma":[0.9849511,0.004790919,0.0006538158,0.0008972439,0.006943127,0.001763875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001523538,0.0003321573,0.01841739,0.0006930565,0.0001063225,0.0006006061,0.001073029,0.0004014689,0.004766059,0.01698346,0.6848095,0.2702934],"study_design_scores_gemma":[0.0005265028,0.0002734428,0.06207087,0.000564843,0.000218778,0.0006855646,0.00115929,0.0006845599,0.004478427,0.008243755,0.9210334,0.00006060374],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1067467,0.01935994,0.0225141,0.1268566,0.01341538,0.001635595,0.01477329,0.0005591909,0.6941391],"genre_scores_gemma":[0.4441606,0.005659114,0.055416,0.01472837,0.003457349,0.002174865,0.009225388,0.0004079745,0.4647704],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04596711,"threshold_uncertainty_score":0.1537753,"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."}}