{"id":"W2793968687","doi":"10.1136/bmj.j5910","title":"Big data and medical research in China","year":2018,"lang":"en","type":"article","venue":"BMJ","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":153,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Peking University People's Hospital; Peking University; University of Alberta","keywords":"Big data; China; Zhàng; Medical research; Data science; Health care; Political science; Medicine; Computer science; Data mining; Law","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005049104,0.00004021405,0.00006917032,0.0001239272,0.00009377418,0.00005723958,0.001577304,0.00005825602,0.00003353213],"category_scores_gemma":[0.001481235,0.00003412402,0.000003632814,0.0004334861,0.000135967,0.0001148487,0.002185879,0.0004164679,0.00009069754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001482141,"about_ca_system_score_gemma":0.0002138441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001694813,"about_ca_topic_score_gemma":0.001046018,"domain_scores_codex":[0.9982322,0.000421754,0.0001423609,0.0003545267,0.0005999806,0.0002492096],"domain_scores_gemma":[0.998465,0.0002329021,0.00001930513,0.001112567,0.0000427458,0.000127477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004284124,0.00001825895,0.09455063,0.00003257705,0.000001408451,0.00008334704,0.0009687051,3.989111e-7,0.000003225446,0.008344914,0.0317892,0.864203],"study_design_scores_gemma":[0.0002434294,0.0001431979,0.4090815,0.0001190996,2.549143e-7,0.00008269152,0.00001942769,0.4206946,0.000006859147,0.004216777,0.1652968,0.00009544498],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3938128,0.000891308,0.05387821,0.4883799,0.002588095,0.0008098705,0.000007446929,0.0002731775,0.05935911],"genre_scores_gemma":[0.9904557,0.00001953873,0.007948995,0.0004080667,0.000958114,0.000005785578,0.000002514094,0.000004332593,0.0001969467],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8641076,"threshold_uncertainty_score":0.2931049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2159307033989999,"score_gpt":0.4922023932229174,"score_spread":0.2762716898239176,"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."}}