{"id":"W4399441519","doi":"10.34133/hds.0112","title":"2023 Beijing Health Data Science Summit","year":2024,"lang":"en","type":"article","venue":"Health Data Science","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Summit; Beijing; Health science; Political science; Geography; China; Medicine; Cartography; Medical education","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.04092937,0.002564658,0.002319516,0.006507195,0.004412746,0.01606575,0.004251461,0.007362316,0.1291472],"category_scores_gemma":[0.04365009,0.0007382561,0.002946345,0.003631324,0.002221303,0.008279205,0.01530899,0.008197822,0.07685404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00827921,"about_ca_system_score_gemma":0.01903009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01176148,"about_ca_topic_score_gemma":0.02091917,"domain_scores_codex":[0.9796651,0.005197705,0.00114032,0.001625772,0.009530229,0.002840864],"domain_scores_gemma":[0.9574956,0.005907825,0.001344264,0.002727298,0.01753983,0.01498528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006568491,0.00001575625,0.0001978344,0.000160515,0.00001753815,0.00004573322,0.00003873204,0.00006965095,0.0001224497,0.001593359,0.9786512,0.0190215],"study_design_scores_gemma":[0.00004461055,0.00004205314,0.001808095,0.0001680952,0.00001195591,0.00001900748,0.0001306902,0.0002372693,0.0001381896,0.002788074,0.9945891,0.00002289761],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.003751302,0.02342172,0.009193024,0.4933462,0.3100731,0.001689735,0.05216625,0.003679003,0.1026796],"genre_scores_gemma":[0.03340432,0.02144025,0.01504627,0.1938466,0.1316886,0.005475316,0.1680278,0.003066849,0.428004],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1291472,"threshold_uncertainty_score":0.4320405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6741552806170339,"score_gpt":0.6558769739566774,"score_spread":0.01827830666035646,"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."}}