{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02218018,0.0008776769,0.001147952,0.004565609,0.004197846,0.009163533,0.001239837,0.006074617,0.004280441],"category_scores_gemma":[0.01621439,0.0004569026,0.001292465,0.007630737,0.007394277,0.007409766,0.005190826,0.006748012,0.0004055382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01208163,"about_ca_system_score_gemma":0.01844915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03798785,"about_ca_topic_score_gemma":0.04356857,"domain_scores_codex":[0.9924885,0.003423133,0.0006621471,0.000488456,0.001751726,0.001185987],"domain_scores_gemma":[0.9724067,0.01519828,0.001816186,0.001052869,0.004306206,0.005219745],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004104634,0.0002375354,0.05866481,0.001882954,0.0007297694,0.00197455,0.009521615,0.00157704,0.00127205,0.2161502,0.4972907,0.2102883],"study_design_scores_gemma":[0.0001317959,0.0001926435,0.0444858,0.001668185,0.0001561011,0.0003621731,0.01129862,0.002199839,0.00089849,0.11676,0.8215415,0.0003049362],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.007296511,0.04198476,0.0009438768,0.934949,0.0101493,0.00003188706,0.0001869848,0.0000427531,0.00441495],"genre_scores_gemma":[0.2467706,0.1420455,0.005023491,0.5394742,0.0466237,0.0002505049,0.0006487833,0.00009924954,0.01906411],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9778198,"threshold_uncertainty_score":0.1173014,"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."}}