{"id":"W7046379375","doi":"","title":"e-ASPECTS software is non-inferior to
\\nneuroradiologists in applying the
\\nASPECT score to computed
\\ntomography scans of acute ischemic
\\nstroke patients","year":2016,"lang":"en","type":"article","venue":"Anglia Ruskin Research Online (Anglia Ruskin University)","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interquartile range; Computed tomography; Stroke (engine); Tomography; Margin (machine learning); Acute stroke; Ground truth; Data set","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":[0.03242608,0.0004889943,0.001001503,0.001020262,0.0003045378,0.001866048,0.0006897864,0.001273716,0.00313341],"category_scores_gemma":[0.06656626,0.0004332119,0.001071715,0.0007905297,0.001084459,0.002233863,0.001503511,0.0005483344,0.001186973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005156597,"about_ca_system_score_gemma":0.0008229383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000607336,"about_ca_topic_score_gemma":0.001668513,"domain_scores_codex":[0.976927,0.01220217,0.002973065,0.003002336,0.004251862,0.0006435627],"domain_scores_gemma":[0.9100233,0.06187629,0.01088024,0.008372309,0.006872386,0.001975528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.1157914,0.001699702,0.6345437,0.0007689098,0.00315656,0.0002093326,0.001026239,0.004156354,0.00748498,0.0009651051,0.003928747,0.2262688],"study_design_scores_gemma":[0.00491051,0.0285816,0.8940475,0.0004160282,0.001708873,0.001523137,0.0008050042,0.04716548,0.008516335,0.002687168,0.009384837,0.0002536525],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802061,0.001379447,0.01096197,0.0004142118,0.0001958395,0.0006358192,0.0004894971,0.0002253598,0.005491687],"genre_scores_gemma":[0.98596,0.0002827476,0.01069685,0.0004243305,0.0001195086,0.0003434657,0.0006702049,0.00011642,0.001386362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03242608,"threshold_uncertainty_score":0.1714875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02749925821534775,"score_gpt":0.2926471897786947,"score_spread":0.2651479315633469,"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."}}