{"id":"W4205170083","doi":"10.1017/cjn.2021.342","title":"P.062 Does the intensity of brain parenchymal contrast staining on post-recanalization dual energy head CT (DECT) of stroke patients predict the fate of brain tissue?","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Parenchyma; Staining; Medicine; Iodine; Brain infarction; Superior sagittal sinus; Nuclear medicine; Stroke (engine); Radiology; Contrast (vision); Infarction; Pathology; Ischemia; Myocardial infarction; Cardiology; Internal medicine; Chemistry","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":[0.000327377,0.0002175676,0.0002366964,0.0002665193,0.0002112574,0.0004674263,0.0002418327,0.0004644899,0.01179683],"category_scores_gemma":[0.002104542,0.0001184776,0.0002896385,0.0003352108,0.0003876779,0.0003765078,0.0001888072,0.0004529287,0.001785063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001520948,"about_ca_system_score_gemma":0.0003439196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007073713,"about_ca_topic_score_gemma":0.0008019125,"domain_scores_codex":[0.9998463,0.0000301293,0.000016745,0.00003623662,0.00002900006,0.00004159735],"domain_scores_gemma":[0.9990007,0.0003327408,0.0003525963,0.00004581537,0.00009983605,0.0001683202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001216339,0.00007864575,0.9907862,0.00002636414,0.00006841279,0.0003545014,0.00003356265,0.00008575029,0.00066393,0.00005982391,0.0007346919,0.00589182],"study_design_scores_gemma":[0.00006822023,0.0007848107,0.9944397,0.00002196418,0.00007050516,0.001978396,0.0001223179,0.001031834,0.0004486792,0.0002782153,0.0007483647,0.000006929814],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960373,0.000234374,0.0002494151,0.0003037413,0.00003834313,0.00001417883,0.0005105654,0.000005781426,0.002606283],"genre_scores_gemma":[0.9990321,0.00005114673,0.0001234633,0.00003816419,0.00004258416,0.00001215906,0.0002204114,0.000003400217,0.000476499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01179683,"threshold_uncertainty_score":0.03946435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01686316469974692,"score_gpt":0.2788432907763376,"score_spread":0.2619801260765907,"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."}}