{"id":"W2902345415","doi":"10.3174/ajnr.a5889","title":"Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning","year":2018,"lang":"en","type":"article","venue":"American Journal of Neuroradiology","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":124,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Ontario Brain Institute","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Intraclass correlation; Confidence interval; Ischemic stroke; Stroke (engine); Area under the curve; Radiology; Effective diffusion coefficient; Area under curve; Nuclear medicine; Ischemia; Magnetic resonance imaging; Internal medicine; Psychometrics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.00168564,0.0005141542,0.0005227953,0.002828483,0.0001759097,0.0006803611,0.0004978221,0.00046234,0.0003817373],"category_scores_gemma":[0.006485645,0.00019623,0.0003414185,0.0009493754,0.0002829185,0.0005582601,0.0004726825,0.0003406245,0.0002038907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003384378,"about_ca_system_score_gemma":0.0004359802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002142354,"about_ca_topic_score_gemma":0.004286834,"domain_scores_codex":[0.9989443,0.0003416332,0.0001270866,0.0002433773,0.0002701466,0.00007340802],"domain_scores_gemma":[0.9971811,0.001146152,0.0006766469,0.0002025136,0.0006806187,0.0001129629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008863516,0.000219446,0.7445868,0.0001281009,0.000227125,0.0002659264,0.0001493482,0.01173356,0.006509521,0.0001170883,0.001169382,0.2340073],"study_design_scores_gemma":[0.00007203895,0.0004836091,0.6913762,0.00006687926,0.0001526796,0.001576793,0.0001722992,0.2964261,0.007567375,0.001058168,0.0009980414,0.00004973606],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9686682,0.0006822743,0.02882375,0.00009384265,0.00002334614,0.0001367947,0.0004227164,0.0003936976,0.0007554743],"genre_scores_gemma":[0.9793113,0.0001915788,0.01966801,0.00003708434,0.00003746556,0.00005812672,0.0005538826,0.00001602546,0.0001265832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002828483,"threshold_uncertainty_score":0.00891459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007662417587762808,"score_gpt":0.2548063589869063,"score_spread":0.2471439413991435,"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."}}