{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001069529,0.0009138832,0.001412326,0.004241178,0.0004145572,0.0000756958,0.002538069,0.0004571461,0.0002556754],"category_scores_gemma":[0.0008388891,0.0007333003,0.0006145941,0.006595782,0.0009801281,0.0004235195,0.002539546,0.001389542,0.0002156723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001297265,"about_ca_system_score_gemma":0.0005753029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003250532,"about_ca_topic_score_gemma":0.002615309,"domain_scores_codex":[0.992151,0.0004073042,0.001012433,0.001937411,0.002286693,0.002205129],"domain_scores_gemma":[0.9941252,0.0007885484,0.0003917199,0.002201722,0.001418063,0.001074776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00352308,0.00189308,0.7999157,0.0003727154,0.001189779,0.00263708,0.001541714,0.00007666078,0.01487606,0.0001918765,0.1554721,0.0183102],"study_design_scores_gemma":[0.01489309,0.004506283,0.7991915,0.003137897,0.0006563368,0.0000667509,0.002212758,0.0003132792,0.02128542,0.0001244829,0.151718,0.001894202],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771581,0.000091947,0.002005048,0.005287892,0.0004141736,0.005135232,0.001251785,0.0002528825,0.008402913],"genre_scores_gemma":[0.9896234,0.0003397432,0.00350128,0.001271632,0.000385129,0.0001057627,0.0002016832,0.0001666964,0.004404673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01641599,"threshold_uncertainty_score":0.9995118,"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."}}