{"id":"W2900458906","doi":"10.1161/strokeaha.118.023457","title":"Highest Lesion Growth Rates in Patients With Hyperacute Stroke","year":2018,"lang":"en","type":"article","venue":"Stroke","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Stroke (engine); Lesion; Internal medicine; Surgery; Cardiology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003118564,0.0002424352,0.0003530681,0.0007144715,0.0003437916,0.0006738119,0.0002526951,0.0004905381,0.001874921],"category_scores_gemma":[0.00342227,0.0001744924,0.0001966381,0.0005855949,0.0003694726,0.0004405082,0.0003896236,0.0005101196,0.0003136769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003826638,"about_ca_system_score_gemma":0.0003466303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001762282,"about_ca_topic_score_gemma":0.001718581,"domain_scores_codex":[0.9997194,0.00005890816,0.0000327359,0.00005741748,0.00007155746,0.00005998364],"domain_scores_gemma":[0.9973848,0.0005101126,0.001326868,0.0001263116,0.0002105485,0.0004412577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002424743,0.00002252504,0.997631,0.000005212834,0.00001351306,0.000397132,0.00005627456,0.00006699251,0.0004091565,0.00001874981,0.00005013432,0.001086797],"study_design_scores_gemma":[0.000009496815,0.0001082949,0.9971651,0.00000342706,0.00001163668,0.001941254,0.0001099831,0.0003056795,0.000175284,0.00006419824,0.00009938127,0.000006303676],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994469,0.000107717,0.00006249465,0.00003096032,0.00000244165,0.000004558729,0.00007013628,0.000004974775,0.0002698946],"genre_scores_gemma":[0.9997168,0.00002605498,0.00005629904,0.00001100678,0.000008536985,0.000003508189,0.0001228861,0.000001279874,0.00005353114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001874921,"threshold_uncertainty_score":0.006272256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138931950228239,"score_gpt":0.2484262605493577,"score_spread":0.2370369410470753,"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."}}