{"id":"W4292014180","doi":"10.21203/rs.3.rs-1920639/v1","title":"Multi-objective learning and explanation for stroke risk assessment in Shanxi province","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Stroke (engine); Recall; Quadratic equation; Set (abstract data type); Computer science; Beijing; Artificial intelligence; Attribution; Blood pressure; Machine learning; Statistics; Mathematics; China; Medicine; Psychology; Engineering; Internal medicine; Cognitive psychology; Geography; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001370455,0.0007356577,0.0005562153,0.001033034,0.0003057891,0.0005213271,0.0007850373,0.0006828096,0.001625341],"category_scores_gemma":[0.003118468,0.0002710427,0.0005980269,0.0006099087,0.0002476892,0.0005151032,0.0006993628,0.0008969452,0.0001663164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001415956,"about_ca_system_score_gemma":0.001943902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06699693,"about_ca_topic_score_gemma":0.04883555,"domain_scores_codex":[0.9996293,0.0001218739,0.00002341807,0.0001177362,0.00004948702,0.0000581264],"domain_scores_gemma":[0.9991412,0.0005068908,0.00007199893,0.00008485279,0.0001225621,0.00007249141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00121517,0.0006000568,0.132866,0.0002582346,0.0005380829,0.000628323,0.0002872598,0.5775032,0.001533271,0.002685541,0.01111057,0.2707743],"study_design_scores_gemma":[0.00003249733,0.00004991287,0.009971399,0.00001196172,0.00003796649,0.00001935889,0.00002568966,0.9874554,0.0004163483,0.001493946,0.0004753161,0.0000103006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8969365,0.002453677,0.09125172,0.002355569,0.0001304156,0.00008792802,0.003305224,0.001507325,0.001971593],"genre_scores_gemma":[0.9858797,0.0002272347,0.01023155,0.0000772332,0.00002928306,0.0000233325,0.002255629,0.00001728375,0.001258789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06699693,"threshold_uncertainty_score":0.1332139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0572986340544719,"score_gpt":0.4354181891286366,"score_spread":0.3781195550741647,"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."}}