{"id":"W4399206797","doi":"10.2196/58491","title":"AI: Bridging Ancient Wisdom and Modern Innovation in Traditional Chinese Medicine","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Traditional Chinese Medicine Studies","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Medicine; Computer science; Data 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005853115,0.000648241,0.0007579998,0.002633562,0.004220938,0.007930548,0.001520608,0.002851937,0.007737709],"category_scores_gemma":[0.005445098,0.0002643404,0.0005247857,0.002635617,0.02403648,0.01126239,0.005090442,0.004261184,0.001182697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005308327,"about_ca_system_score_gemma":0.00790847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004502595,"about_ca_topic_score_gemma":0.00421756,"domain_scores_codex":[0.9974421,0.00124747,0.0001467206,0.0003696832,0.0005800622,0.0002139766],"domain_scores_gemma":[0.9963499,0.002379756,0.0002040903,0.0002644036,0.0003719717,0.0004298728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005897421,0.00004403128,0.001169655,0.001061718,0.00003785167,0.000450043,0.01156919,0.0006432339,0.0005386505,0.8150895,0.02216108,0.1471761],"study_design_scores_gemma":[0.00002391131,0.00006147122,0.001676796,0.001023578,0.00002268935,0.000321049,0.004023314,0.001316976,0.0002773785,0.7283826,0.2628246,0.00004570729],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03042288,0.2496879,0.06856751,0.3473275,0.007657856,0.0002407652,0.0002623579,0.0004293675,0.2954039],"genre_scores_gemma":[0.6801045,0.1781255,0.04788579,0.03564816,0.01194403,0.0005127295,0.0002408782,0.0002139065,0.04532465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007930548,"threshold_uncertainty_score":0.03851479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03125304149741721,"score_gpt":0.3299777781991348,"score_spread":0.2987247367017176,"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."}}