{"id":"W3033070441","doi":"10.2196/18780","title":"Medical Insurance Information Systems in China: Mixed Methods Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Healthcare Systems and Reforms","field":"Economics, Econometrics and Finance","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Informatization; China; Business; Status quo; Information system; Computer science; Political 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004270772,0.0002205668,0.0008431738,0.0002656766,0.00008287891,0.0001047753,0.0005718582,0.0004782485,0.0002191027],"category_scores_gemma":[0.001407121,0.0001754863,0.00009078156,0.0007472209,0.0000716922,0.00106453,0.0001849388,0.0008610243,0.000916189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001298221,"about_ca_system_score_gemma":0.0001987702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001321754,"about_ca_topic_score_gemma":0.00004422601,"domain_scores_codex":[0.9954867,0.0001208256,0.003280988,0.0001566287,0.0004914387,0.000463371],"domain_scores_gemma":[0.9980761,0.00005884252,0.0006812283,0.0003022874,0.00004938985,0.0008320851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00008770705,0.00084746,0.3184702,0.004997627,0.0001999344,0.00008345109,0.1871914,0.0002200451,3.520562e-7,0.04827963,0.006709457,0.4329128],"study_design_scores_gemma":[0.005391577,0.0008244516,0.4942419,0.0004750019,0.0000028758,0.00006934792,0.03148872,0.2232453,0.000002753115,0.0004837429,0.2428942,0.0008801639],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9518186,0.0006188921,0.02313542,0.004099289,0.002238246,0.001948088,0.00007881616,0.0001881528,0.01587445],"genre_scores_gemma":[0.9964506,0.0001233947,0.0007134277,0.002198577,0.00024653,0.0001978178,0.00002900267,0.00001526825,0.00002539952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4320326,"threshold_uncertainty_score":0.9998617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03559234592310894,"score_gpt":0.3297426039212558,"score_spread":0.2941502579981469,"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."}}