{"id":"W3163919899","doi":"10.2196/26393","title":"Measuring the Interactions Between Health Demand, Informatics Supply, and Technological Applications in Digital Medical Innovation for China: Content Mapping and Analysis","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing Nova Program; National Natural Science Foundation of China; China Association for Science and Technology","keywords":"Health informatics; Informatics; Public health informatics; Business informatics; The Internet; Data science; Knowledge management; China; Computer science; Node (physics); Business; Public health; Medicine; World Wide Web; Health policy; Political science; International health; Engineering; Nursing","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":[0.00295852,0.0004052949,0.0004952682,0.0230972,0.000840056,0.002044527,0.000584622,0.0005402247,0.002056025],"category_scores_gemma":[0.01098208,0.0002181025,0.0008835563,0.02728765,0.0007507514,0.0026418,0.001845484,0.0003788935,0.0003358254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003304379,"about_ca_system_score_gemma":0.003106507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0218413,"about_ca_topic_score_gemma":0.01416961,"domain_scores_codex":[0.9977562,0.0004957001,0.0003656982,0.0003963838,0.0007446962,0.0002412559],"domain_scores_gemma":[0.983389,0.009797726,0.002905146,0.0005892364,0.002755937,0.0005629422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001094454,0.00008868117,0.9211646,0.0008860463,0.000179179,0.0002690686,0.006434028,0.001666321,0.001621892,0.00257677,0.001859657,0.06314438],"study_design_scores_gemma":[0.000008894191,0.00006881297,0.9700244,0.0001167645,0.0001742441,0.0001153755,0.007802801,0.01462108,0.001084176,0.001215357,0.004735318,0.00003273431],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875264,0.0003977962,0.00216245,0.0003484353,0.00001001513,0.0001320378,0.005913596,0.00005735823,0.00345196],"genre_scores_gemma":[0.9889242,0.0003321922,0.003973471,0.00003915982,0.0000239825,0.0002551781,0.005556972,0.00001662947,0.0008781395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0230972,"threshold_uncertainty_score":0.04342836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05424437482597525,"score_gpt":0.3373264061078717,"score_spread":0.2830820312818965,"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."}}