{"id":"W4406337282","doi":"10.1016/j.nicl.2025.103732","title":"A deep learning approach versus expert clinician panel in the classification of posterior circulation infarction","year":2025,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"Infarction; Circulation (fluid dynamics); Medicine; Neuroscience; Internal medicine; Psychology; Cardiology; Myocardial infarction; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001121746,0.0001166905,0.0003015376,0.0001510507,0.00004494983,0.00001738203,0.0001746631,0.0001407118,0.00001377407],"category_scores_gemma":[0.002067939,0.00009369843,0.0001588208,0.0003879963,0.0001374227,0.00007023937,0.00008115487,0.0006043529,0.0000107616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004670086,"about_ca_system_score_gemma":0.0000480184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001657139,"about_ca_topic_score_gemma":0.000003014524,"domain_scores_codex":[0.9979225,0.000400476,0.0008614262,0.0003910198,0.0002669753,0.0001575949],"domain_scores_gemma":[0.9984033,0.0007566486,0.00023552,0.0004910796,0.00008022505,0.00003319449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004195843,0.001872504,0.5922929,0.0002413989,0.0001918837,0.00003872002,0.001563053,0.0001662695,0.01905316,0.0004287779,0.004029533,0.375926],"study_design_scores_gemma":[0.002970186,0.0003046581,0.9578653,0.00004624544,0.0001017712,0.000006697717,0.001167879,0.02859102,0.00002936091,0.000007112892,0.008846033,0.00006376095],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9632035,0.00009377598,0.003447663,0.003633935,0.00053383,0.0007100068,5.440462e-7,0.0000487079,0.02832803],"genre_scores_gemma":[0.9967015,0.0001091721,0.0009910815,0.001771929,0.0001645374,0.0000431105,0.00003986287,0.00001282105,0.0001659263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3758622,"threshold_uncertainty_score":0.3820911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1441716000101467,"score_gpt":0.4027158909575729,"score_spread":0.2585442909474263,"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."}}