{"id":"W4313828125","doi":"10.1161/strokeaha.122.041302","title":"Using Noncontrast Computed Tomography to Improve Prediction of Intracerebral Hemorrhage Expansion","year":2023,"lang":"en","type":"article","venue":"Stroke","topic":"Intracerebral and Subarachnoid Hemorrhage Research","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Population Health Research Institute","funders":"National Institute of Neurological Disorders and Stroke; Avid Radiopharmaceuticals; National Institutes of Health; Boston Scientific Corporation; AstraZeneca; CSL Behring; Pfizer Canada; Alexion Pharmaceuticals; Biogen; Pfizer; Heart and Stroke Foundation of Canada; McMaster University; American Heart Association","keywords":"Medicine; Retrospective cohort study; Intracerebral hemorrhage; Logistic regression; Radiology; Receiver operating characteristic; Odds ratio; Computed tomography angiography; Hematoma; Confounding; Prospective cohort study; Computed tomography; Nuclear medicine; Surgery; Subarachnoid hemorrhage; Internal medicine","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":[0.001685093,0.0005881335,0.0003667827,0.001269837,0.0001467269,0.0005620444,0.0004477179,0.0003387246,0.000876953],"category_scores_gemma":[0.00672662,0.0001839613,0.0004811663,0.0008978799,0.0002886257,0.0005296153,0.0004121963,0.0005231278,0.0002405562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002616756,"about_ca_system_score_gemma":0.0004946334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001682618,"about_ca_topic_score_gemma":0.002245484,"domain_scores_codex":[0.9993134,0.0002696209,0.00009155848,0.0001118337,0.0001425544,0.00007103296],"domain_scores_gemma":[0.9949929,0.002100414,0.001832482,0.000291144,0.0003976359,0.0003855438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001675472,0.00001957196,0.9982442,0.000006768239,0.00002210037,0.00003725302,0.000007909418,0.0001242779,0.00006011211,0.000005654304,0.00002752298,0.001277153],"study_design_scores_gemma":[0.00001919605,0.0003066314,0.9947226,0.00001374609,0.00006593086,0.0004233822,0.0000391571,0.003906915,0.0003141823,0.00004308029,0.0001391684,0.000006018966],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986532,0.0003307729,0.0004011654,0.00005227143,0.00001153426,0.00001315832,0.0002156531,0.00001159538,0.0003107167],"genre_scores_gemma":[0.9993229,0.00008500868,0.0002937607,0.000008632082,0.00001485429,0.000005245052,0.0002157524,0.000001639009,0.0000521306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001685093,"threshold_uncertainty_score":0.008911729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02984999612846173,"score_gpt":0.2985199552142447,"score_spread":0.268669959085783,"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."}}