{"id":"W2913394566","doi":"10.1161/str.50.suppl_1.wp182","title":"Abstract WP182: Machine Learning Models are More Accurate Than Regression-based Models for Predicting Functional Impairment Risk in Acute Ischemic Stroke","year":2019,"lang":"en","type":"article","venue":"Stroke","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Medicine; Interquartile range; Receiver operating characteristic; Stroke (engine); Modified Rankin Scale; Regression; Regression analysis; Cohort; Machine learning; Physical therapy; Internal medicine; Ischemic stroke; Statistics","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":[0.0219641,0.001538792,0.001683585,0.002140972,0.0004808731,0.003611659,0.0009552749,0.001804241,0.00928514],"category_scores_gemma":[0.07832037,0.0004555518,0.001926329,0.001876404,0.0007533417,0.003242141,0.001082145,0.002490662,0.00274224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007661435,"about_ca_system_score_gemma":0.001116553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002327552,"about_ca_topic_score_gemma":0.001506948,"domain_scores_codex":[0.9883959,0.007527649,0.0006796333,0.00145155,0.0016452,0.0003000222],"domain_scores_gemma":[0.947321,0.04094251,0.00434614,0.002652211,0.004168005,0.0005701694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007231016,0.00106823,0.5525762,0.00140924,0.005045914,0.0005529264,0.0003842356,0.1164009,0.001484858,0.002588702,0.05216612,0.2590918],"study_design_scores_gemma":[0.001005901,0.002929777,0.2955213,0.001240841,0.002148024,0.000998013,0.0003844492,0.6569629,0.002528748,0.0214745,0.01459219,0.0002133642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8019197,0.01530355,0.1247687,0.01704936,0.002323805,0.000683683,0.01107165,0.001922926,0.02495655],"genre_scores_gemma":[0.9765371,0.001031583,0.01177658,0.001294303,0.0007923853,0.0002112694,0.004730094,0.0002508908,0.003375668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0219641,"threshold_uncertainty_score":0.1161586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02411353186841501,"score_gpt":0.2697318029042786,"score_spread":0.2456182710358636,"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."}}