{"id":"W2745785581","doi":"","title":"日本と米国におけるてんかんの薬物治療； 各国の治療，ガイドライン，薬剤選択について","year":2010,"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","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005400309,0.0004802447,0.0004829964,0.0002341424,0.0002212811,0.00003989253,0.0008724788,0.000752018,0.01327774],"category_scores_gemma":[0.0002286931,0.0004838937,0.0001734731,0.0003613854,0.0005678943,0.0002919456,0.0001361555,0.002851706,0.002207807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003227463,"about_ca_system_score_gemma":0.0001131535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006667584,"about_ca_topic_score_gemma":0.00009397163,"domain_scores_codex":[0.997575,0.00005304228,0.0005508788,0.0005000763,0.0004289076,0.0008920372],"domain_scores_gemma":[0.998494,0.0001657277,0.00006744793,0.0007914655,0.00005825516,0.0004231535],"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":[0.0002520361,0.0009004847,0.003703746,0.001900435,0.001978981,0.00220831,0.007576591,0.0003099686,0.2568527,0.1478936,0.3984641,0.1779591],"study_design_scores_gemma":[0.003815762,0.0002716415,0.003569279,0.0002162991,0.0004795986,0.000556316,0.001282662,0.03114184,0.02699328,0.02003934,0.9097214,0.001912603],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.532207,0.0112714,0.0004386185,0.006539187,0.01508093,0.0005535622,0.0001087846,0.002478719,0.4313218],"genre_scores_gemma":[0.9942259,0.001809412,0.0009322866,0.0003688839,0.001281202,0.00004533194,0.00002895462,0.00007482478,0.001233267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5112574,"threshold_uncertainty_score":0.9997613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01242620742157547,"score_gpt":0.2551619172801331,"score_spread":0.2427357098585576,"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."}}