{"id":"W2098856112","doi":"10.1109/tbme.2006.888834","title":"Simulation of Biphasic CT Findings in Hepatic Cellular Carcinoma by a Two-Level Physiological Model","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Hepatocellular carcinoma; Contrast (vision); Computed tomography; Vascular network; Biomedical engineering; Pathological; Hepatic carcinoma; Carcinoma; Sequence (biology); In vivo; Computer science; Radiology; Pathology; Artificial intelligence; Medicine; Anatomy; Biology; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002222481,0.0004995229,0.0003868172,0.0004398421,0.0002232675,0.0007178119,0.0006820249,0.001465954,0.001252588],"category_scores_gemma":[0.001275619,0.0003577408,0.0005862587,0.0002687989,0.0006330331,0.0005216462,0.0006052814,0.0005003053,0.0002054777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006396311,"about_ca_system_score_gemma":0.0006482985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005771503,"about_ca_topic_score_gemma":0.001717326,"domain_scores_codex":[0.9999018,0.00003102372,0.000006070863,0.00001640394,0.00002903346,0.00001558509],"domain_scores_gemma":[0.9996723,0.000178533,0.00004466072,0.00002365759,0.00004617703,0.00003477808],"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":[0.00002700303,0.00001936761,0.00043372,0.00002242168,0.00000518892,0.0001489433,0.00003066341,0.990851,0.00439622,0.002883265,0.00006855036,0.001113665],"study_design_scores_gemma":[0.000007601761,0.00001359331,0.0001143951,0.000002125548,0.000002709636,0.00002879839,0.000004888843,0.9988627,0.0003835091,0.0004478808,0.0001277576,0.000004038362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3395376,0.0004736974,0.6493083,0.0007472667,0.00009449864,0.0001370917,0.0003705175,0.0005909214,0.008740209],"genre_scores_gemma":[0.9629453,0.000360918,0.03265731,0.0000981679,0.00001866665,0.0001915173,0.0001470348,0.00004919411,0.003531828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005771503,"threshold_uncertainty_score":0.01147586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04250702776586123,"score_gpt":0.2874583160733036,"score_spread":0.2449512883074423,"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."}}