{"id":"W3139421235","doi":"10.1161/str.52.suppl_1.p330","title":"Abstract P330: Optimizing Predictions of Infarct Core Using Machine Learning","year":2021,"lang":"en","type":"article","venue":"Stroke","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Stroke (engine); Perfusion scanning; Occlusion; Radiology; Gold standard (test); Nuclear medicine; Computed tomography; Perfusion; Surgery","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001912131,0.001427668,0.0007114398,0.000727653,0.0002536761,0.0006491566,0.0008904499,0.00112189,0.002829625],"category_scores_gemma":[0.00354559,0.000295297,0.0006031846,0.000399251,0.0003983047,0.0006712753,0.0006718961,0.0008626003,0.0007809183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007039241,"about_ca_system_score_gemma":0.001460675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004930275,"about_ca_topic_score_gemma":0.002650982,"domain_scores_codex":[0.9995878,0.0001368781,0.00002550253,0.0001409339,0.00004915413,0.00005959278],"domain_scores_gemma":[0.998782,0.0006578658,0.0001365833,0.00006362189,0.0002579243,0.0001021295],"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":[0.001294878,0.0008478355,0.05493253,0.0001792786,0.0003132466,0.0002040425,0.00004407816,0.731298,0.003723126,0.0008651725,0.010075,0.1962227],"study_design_scores_gemma":[0.00003519487,0.0001312265,0.002027601,0.000007430753,0.00001372375,0.0000253327,0.000005216996,0.9961449,0.0008149582,0.0006476233,0.0001415506,0.00000531802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.768712,0.001305392,0.219069,0.001905894,0.0002042779,0.000329833,0.002214019,0.002355509,0.003904072],"genre_scores_gemma":[0.9708742,0.0001572553,0.02524623,0.0001713908,0.0001036947,0.0001153434,0.001898152,0.00004935292,0.001384246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004930275,"threshold_uncertainty_score":0.01011246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03500491630207684,"score_gpt":0.2923413779015257,"score_spread":0.2573364615994488,"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."}}