{"id":"W4388892778","doi":"10.1093/ehjdh/ztad073","title":"Development and internal validation of machine learning–based models and external validation of existing risk scores for outcome prediction in patients with ischaemic stroke","year":2023,"lang":"en","type":"article","venue":"European Heart Journal - Digital Health","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"National Institute on Minority Health and Health Disparities","keywords":"Medicine; Random forest; Receiver operating characteristic; Support vector machine; Gradient boosting; Predictive modelling; Stroke (engine); Outcome (game theory); Machine learning; Failure to thrive; Artificial intelligence; Internal medicine; Computer science; Engineering; Mathematics","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":[0.03578182,0.001338615,0.0008054279,0.001198483,0.0004361292,0.001715815,0.001269424,0.0009811443,0.0007785525],"category_scores_gemma":[0.06255688,0.0004314287,0.0015498,0.0007093056,0.0007426448,0.001223031,0.00174462,0.002020334,0.0005907191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009511518,"about_ca_system_score_gemma":0.002133888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001913104,"about_ca_topic_score_gemma":0.002234043,"domain_scores_codex":[0.9887449,0.007054635,0.001020499,0.001149254,0.00163455,0.0003960535],"domain_scores_gemma":[0.9646589,0.02095159,0.003300849,0.003335159,0.007098324,0.0006551452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001557716,0.001463397,0.7691795,0.0002232135,0.00130822,0.000164109,0.0005170686,0.09817722,0.001914853,0.00107495,0.003168985,0.1212509],"study_design_scores_gemma":[0.0004251906,0.002447882,0.2106151,0.0002949209,0.0005343701,0.0003507501,0.0001758304,0.7730217,0.007305025,0.002553455,0.002177141,0.00009863909],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8665413,0.0007306387,0.1268394,0.0008604267,0.0001764668,0.0005817355,0.001061023,0.0005726354,0.002636484],"genre_scores_gemma":[0.972673,0.0001364718,0.02498281,0.000210591,0.00007087462,0.0003591412,0.001210228,0.00004352875,0.0003134172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03578182,"threshold_uncertainty_score":0.1892346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0723013014128489,"score_gpt":0.3224504234544739,"score_spread":0.250149122041625,"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."}}