{"id":"W4379279926","doi":"10.1017/cjn.2023.215","title":"P.126 Prediction of cerebral vasospasm using radiographical and clinical features: a machine learning model","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Intracranial Aneurysms: Treatment and Complications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Subarachnoid hemorrhage; Vasospasm; Radiology; Digital subtraction angiography; Transcranial Doppler; Middle cerebral artery; Cerebral vasospasm; Cardiology; Internal medicine; Angiography; Ischemia","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001584186,0.0008895904,0.0006150036,0.001251338,0.0003088144,0.001247977,0.0009274362,0.001049833,0.005599074],"category_scores_gemma":[0.005659034,0.0002645048,0.001061182,0.000853907,0.0003401116,0.0006258977,0.0003799085,0.001487839,0.002469839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005977252,"about_ca_system_score_gemma":0.0006475513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006316597,"about_ca_topic_score_gemma":0.003005367,"domain_scores_codex":[0.9996246,0.0001176773,0.00002981214,0.0001084032,0.00007347958,0.00004592104],"domain_scores_gemma":[0.9976489,0.001644016,0.0001706114,0.00007967797,0.0003770987,0.0000796614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009177871,0.0009551844,0.1329239,0.0002596088,0.000419651,0.0006844459,0.0001133208,0.5328318,0.001889178,0.001927039,0.02739847,0.2996796],"study_design_scores_gemma":[0.00002632212,0.0000998766,0.007073431,0.00003297581,0.00003185955,0.000124419,0.00001042938,0.9891949,0.0002935572,0.002398334,0.0007009843,0.00001285295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6089436,0.00459804,0.3413042,0.01116286,0.001158235,0.0007380394,0.011075,0.003826533,0.01719345],"genre_scores_gemma":[0.958622,0.0005889516,0.0292285,0.0005230667,0.0003952023,0.0002495715,0.003897605,0.00008258228,0.006412504],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006316597,"threshold_uncertainty_score":0.01873082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06307497617511588,"score_gpt":0.3084815859627331,"score_spread":0.2454066097876172,"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."}}