{"id":"W3136897777","doi":"10.1161/strokeaha.120.033785","title":"Challenges of Outcome Prediction for Acute Stroke Treatment Decisions","year":2021,"lang":"en","type":"article","venue":"Stroke","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Brain Institute; University of Calgary","funders":"","keywords":"Outcome (game theory); Medicine; Stroke (engine); Intensive care medicine; Acute stroke; Perspective (graphical); Predictive modelling; Clinical decision making; Artificial intelligence; Psychiatry; Machine learning; Computer science","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":[0.07984474,0.001667892,0.002915011,0.002989102,0.002178639,0.01018159,0.004853036,0.003488191,0.001925985],"category_scores_gemma":[0.2114268,0.001307853,0.001898321,0.002301642,0.007496976,0.009263916,0.006603893,0.01062392,0.0009082218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005663774,"about_ca_system_score_gemma":0.008631337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01387222,"about_ca_topic_score_gemma":0.007843474,"domain_scores_codex":[0.9381646,0.04811458,0.003037036,0.003853058,0.005737847,0.001092896],"domain_scores_gemma":[0.7604859,0.2140832,0.00925047,0.005565489,0.008178161,0.002436805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000402393,0.0004849614,0.04195356,0.002315199,0.001088523,0.0006900459,0.003654622,0.1626831,0.0003629204,0.4257042,0.03835725,0.3223032],"study_design_scores_gemma":[0.00004737809,0.00007861186,0.002608232,0.001227914,0.0001031274,0.0001706788,0.001029843,0.1256504,0.00015239,0.8594722,0.009359058,0.0001001765],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.04984942,0.03063273,0.398913,0.4980898,0.001888506,0.0004731884,0.002003135,0.000463711,0.01768647],"genre_scores_gemma":[0.7402214,0.01509156,0.2261428,0.01138,0.004077195,0.0006316892,0.00111816,0.0001424479,0.00119481],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.07984474,"threshold_uncertainty_score":0.4222644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06597010603553181,"score_gpt":0.3440679189056118,"score_spread":0.27809781287008,"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."}}