{"id":"W3204863995","doi":"10.3390/biomedicines9101357","title":"Treatment Efficacy Analysis in Acute Ischemic Stroke Patients Using In Silico Modeling Based on Machine Learning: A Proof-of-Principle","year":2021,"lang":"en","type":"article","venue":"Biomedicines","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; Hotchkiss Brain Institute; University of Calgary","funders":"Canada Research Chairs; Heart and Stroke Foundation of Canada","keywords":"Proof of concept; In silico; Stroke (engine); Ischemic stroke; Machine learning; Medicine; Computer science; Artificial intelligence; Cardiology; Ischemia; Engineering; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004552752,0.0006907399,0.001035604,0.0002438721,0.0001374942,0.0005929588,0.0007720819,0.0008044852,0.0009831407],"category_scores_gemma":[0.00752484,0.0003612608,0.001284862,0.0001234932,0.0005244671,0.0004396216,0.0004474037,0.000670277,0.0001476314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000284785,"about_ca_system_score_gemma":0.0007985458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002805376,"about_ca_topic_score_gemma":0.0001705583,"domain_scores_codex":[0.9983355,0.001279882,0.00005224011,0.0001290387,0.0001516002,0.00005171221],"domain_scores_gemma":[0.9950364,0.004081201,0.000385269,0.0002802131,0.0001526602,0.00006417596],"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":[0.003607507,0.003395821,0.009476639,0.0008929778,0.001093957,0.0002487853,0.0001235072,0.7711018,0.06607655,0.01099392,0.001277346,0.1317112],"study_design_scores_gemma":[0.0007956076,0.006932445,0.002476111,0.00004358286,0.0003805545,0.0001533891,0.00001815801,0.9647829,0.01727971,0.005569593,0.001519823,0.00004810332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1896596,0.0006159409,0.805712,0.0007848641,0.0001376856,0.0008912607,0.000211743,0.0003404208,0.001646389],"genre_scores_gemma":[0.8694541,0.000451153,0.1276934,0.000309905,0.0001009468,0.001249047,0.0001838473,0.00004548517,0.0005119945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004552752,"threshold_uncertainty_score":0.02407753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02674287925347144,"score_gpt":0.3057375639440582,"score_spread":0.2789946846905867,"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."}}