{"id":"W2899136888","doi":"10.1136/bmjebm-2018-111089","title":"Understanding Risk for Better Stroke Prevention","year":2018,"lang":"en","type":"letter","venue":"BMJ evidence-based medicine","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; University of Toronto; St. Michael's Hospital","funders":"","keywords":"Stroke (engine); Medicine; Physical medicine and rehabilitation; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"commentary","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"commentary","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006817156,0.00165145,0.002538839,0.002260497,0.003399669,0.004670719,0.003701977,0.04348125,0.03147244],"category_scores_gemma":[0.06637035,0.0009507808,0.002334961,0.001535098,0.004470737,0.009986634,0.002906017,0.0556946,0.03005958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006601144,"about_ca_system_score_gemma":0.008738963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01281899,"about_ca_topic_score_gemma":0.01273012,"domain_scores_codex":[0.99295,0.002614093,0.0009619379,0.000649579,0.002276528,0.0005479823],"domain_scores_gemma":[0.9668623,0.01861421,0.001288756,0.0007415031,0.009219508,0.003273773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000159923,0.00000866764,0.00005640859,0.00007146829,0.000006001895,0.00006858673,0.00003746476,0.00001168736,0.00001256286,0.0009062946,0.9947016,0.00410332],"study_design_scores_gemma":[0.0002564102,0.00006042213,0.0006608163,0.002431531,0.00005414697,0.0005925847,0.0003808001,0.0001632545,0.00008240106,0.01357918,0.9816663,0.00007222671],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.00003347249,0.002756763,0.00003254309,0.9699954,0.02603563,0.000007497926,0.00008136598,0.00001704793,0.001040214],"genre_scores_gemma":[0.0007647603,0.003517651,0.0001055088,0.9087889,0.08407494,0.00003651529,0.00007608414,0.00002007379,0.002615545],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.04348125,"threshold_uncertainty_score":0.1052858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2490737231881227,"score_gpt":0.3807631036811311,"score_spread":0.1316893804930084,"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."}}