{"id":"W4281710423","doi":"10.1186/s12880-022-00825-2","title":"Practical utility of liver segmentation methods in clinical surgeries and interventions","year":2022,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Qatar National Research Fund; Fonds National de la Recherche Luxembourg; Qatar Foundation","keywords":"Medicine; Radiology; Segmentation; Magnetic resonance imaging; Liver transplantation; Hepatocellular carcinoma; Radiological weapon; Surgical planning; Radiation treatment planning; Medical physics; Psychological intervention; Radiation therapy; Computer science; Transplantation; Artificial intelligence; Surgery","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":[0.009242714,0.001744987,0.001010177,0.006793754,0.0008596522,0.004451411,0.001580404,0.002660704,0.00518224],"category_scores_gemma":[0.03559257,0.0009009805,0.001383032,0.003747118,0.002102832,0.002590839,0.002088892,0.001495593,0.004169207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001173027,"about_ca_system_score_gemma":0.002125316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002205579,"about_ca_topic_score_gemma":0.002170177,"domain_scores_codex":[0.9922241,0.003540752,0.0007890329,0.001018208,0.002229925,0.00019783],"domain_scores_gemma":[0.9805684,0.01213889,0.001732847,0.001926614,0.003269102,0.000364053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003548576,0.00007077297,0.01423736,0.00159753,0.0001381793,0.0004701083,0.0006481663,0.01533798,0.008047079,0.01181903,0.007845505,0.9394335],"study_design_scores_gemma":[0.000275639,0.00123636,0.06144736,0.00617485,0.001096609,0.01347965,0.00248503,0.3649425,0.07429849,0.2172801,0.2564828,0.0008006637],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02128382,0.05706036,0.8920849,0.005238442,0.0008063585,0.000430256,0.0007928485,0.002794638,0.01950836],"genre_scores_gemma":[0.2628305,0.02856125,0.7019334,0.0008966197,0.001036195,0.0003484204,0.0008781127,0.0006801029,0.002835382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009242714,"threshold_uncertainty_score":0.0488807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3273435613522385,"score_gpt":0.4977588032630776,"score_spread":0.1704152419108391,"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."}}