{"id":"W2743243578","doi":"","title":"胃癌:低侵襲治療と集学的治療の個別化へ向けて 胃癌の化学療法と分子標的治療","year":2017,"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001236794,0.0002642411,0.0001995934,0.0006765234,0.001692125,0.003473591,0.0004606911,0.001081815,0.01053137],"category_scores_gemma":[0.002211204,0.0002156067,0.0002561476,0.0004467027,0.004707342,0.002182962,0.0009208039,0.001339898,0.003290239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001733351,"about_ca_system_score_gemma":0.002595201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003879319,"about_ca_topic_score_gemma":0.002728547,"domain_scores_codex":[0.9992996,0.0001340575,0.00004666092,0.0001399145,0.0003034479,0.00007619848],"domain_scores_gemma":[0.998993,0.0002737594,0.0001274798,0.00009649771,0.0003737005,0.0001355797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001114983,0.0001042584,0.002836891,0.000259227,0.00003656599,0.0004856809,0.004677648,0.0007080556,0.007131358,0.8213441,0.02015966,0.1421449],"study_design_scores_gemma":[0.00004234568,0.0002484222,0.006719186,0.0002721186,0.00007782831,0.001057822,0.005821139,0.001155322,0.01781735,0.344068,0.6226272,0.00009331402],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07976433,0.01222339,0.03782529,0.02536385,0.003882724,0.0002541522,0.0002843122,0.0001959837,0.840206],"genre_scores_gemma":[0.7287711,0.006830426,0.01798961,0.004091769,0.001236892,0.0001459984,0.0001225718,0.00005892931,0.2407528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01053137,"threshold_uncertainty_score":0.03523093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02388063563345101,"score_gpt":0.2831049655504125,"score_spread":0.2592243299169615,"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."}}