{"id":"W2176855793","doi":"10.1016/j.neurad.2015.09.003","title":"Prediction of the consistency of pituitary adenoma: A comparative study on diffusion-weighted imaging and pathological results","year":2015,"lang":"en","type":"article","venue":"Journal of Neuroradiology","topic":"Pituitary Gland Disorders and Treatments","field":"Medicine","cited_by":71,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Ministère de l'Éducation, du Loisir et du Sport Québec","keywords":"Pituitary adenoma; Diffusion MRI; Pathological; Consistency (knowledge bases); Medicine; Adenoma; Magnetic resonance imaging; Radiology; Artificial intelligence; Pathology; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001973794,0.00009137961,0.0004647303,0.00009180544,0.000032253,0.000001499778,0.00006398883,0.00003548973,0.000001784628],"category_scores_gemma":[0.0001955735,0.00004586584,0.00007231304,0.00008676662,0.0001819338,0.0000354322,0.0000348989,0.0001820077,3.379028e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000157415,"about_ca_system_score_gemma":0.00006817494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009084099,"about_ca_topic_score_gemma":0.000001527724,"domain_scores_codex":[0.998875,0.0002710538,0.0004810136,0.0001128464,0.0001746134,0.00008553637],"domain_scores_gemma":[0.9990495,0.0001634265,0.0003946647,0.0001393129,0.0001698016,0.00008325571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003398281,0.001920833,0.9890922,0.000008107969,0.0001152945,0.0004471047,0.002005387,0.000006273495,0.001787265,0.0000286966,0.0009547147,0.0002358889],"study_design_scores_gemma":[0.009600521,0.00901956,0.9768764,0.00004359336,0.0001921946,0.001604443,0.002041701,0.00009370783,0.00007738517,0.0003794522,0.00004308546,0.00002793718],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974232,0.0006905861,0.000006712081,0.0008095214,0.0002286091,0.0002325852,0.00002749968,0.000003247544,0.0005780428],"genre_scores_gemma":[0.9997367,0.00004673164,0.00005060632,0.000106619,0.00004272378,0.000001222078,0.000002608057,0.000003390175,0.000009377176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01221573,"threshold_uncertainty_score":0.1870355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07836063195346832,"score_gpt":0.3020147499188064,"score_spread":0.2236541179653381,"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."}}