{"id":"W2748179737","doi":"","title":"大腸癌診療update 2016 抗EGFR抗体のエビデンスupdate","year":2016,"lang":"ja","type":"article","venue":"Pharma Medica","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004700474,0.0004401993,0.000441898,0.00020459,0.000140928,0.00002041361,0.000740364,0.0004046627,0.0139006],"category_scores_gemma":[0.0001262982,0.0003022202,0.0001411936,0.0002410201,0.0005115448,0.0004324929,0.0001533252,0.0005144359,0.007363654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008500273,"about_ca_system_score_gemma":0.0001084155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002285189,"about_ca_topic_score_gemma":0.000008976875,"domain_scores_codex":[0.9976127,0.00008731899,0.0005457737,0.0004960374,0.0003525147,0.0009056383],"domain_scores_gemma":[0.9986621,0.0001773702,0.00007222016,0.0006852458,0.00005018563,0.0003529122],"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":[0.0001536694,0.0001810168,0.0004997959,0.0004196017,0.001015464,0.0006825704,0.0008396559,0.00002554972,0.05604319,0.03091685,0.6135986,0.295624],"study_design_scores_gemma":[0.003681766,0.000160478,0.000591182,0.0008406878,0.0002423525,0.0001215718,0.0002542822,0.001350129,0.02831008,0.0174878,0.9458268,0.001132814],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1861208,0.07793972,0.01242389,0.07008383,0.01816095,0.001359289,0.001097306,0.00659863,0.6262156],"genre_scores_gemma":[0.9698324,0.02342365,0.0003201763,0.000381386,0.0006601943,0.00003802407,0.0000161264,0.00007009407,0.005257968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7837116,"threshold_uncertainty_score":0.999943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01352597095067617,"score_gpt":0.249232024050025,"score_spread":0.2357060530993488,"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."}}