{"id":"W2998042392","doi":"","title":"A Comparative Study Between Apparent Diffusion Imaging and Correlated Diffusion Imaging for Prostate Cancer","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Prostate cancer; Medicine; Magnetic resonance imaging; Grading (engineering); Effective diffusion coefficient; Histopathology; Diffusion MRI; Cancer; Cancer detection; Radiology; Modality (human–computer interaction); Prostate; Diffusion-Weighted Magnetic Resonance Imaging; Diffusion imaging; Nuclear medicine; Pathology; Artificial intelligence; Internal medicine; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006395528,0.0002411322,0.000765669,0.0003524982,0.0001756251,0.000185825,0.00008112218,0.00002471508,0.00001520421],"category_scores_gemma":[0.00002648168,0.0001809256,0.00009402914,0.0001684457,0.00006797736,0.0003436011,0.0000814915,0.0002604253,0.000002715448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000176951,"about_ca_system_score_gemma":0.0001372032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001174683,"about_ca_topic_score_gemma":0.000001455466,"domain_scores_codex":[0.9978704,0.0001270983,0.0008528109,0.0002863723,0.0006389612,0.0002243156],"domain_scores_gemma":[0.9976075,0.0005561918,0.0007337294,0.0001093914,0.0007839852,0.0002092306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004257768,0.000254038,0.9759728,0.0002767408,0.0001234862,0.0000185748,0.003658563,0.002297269,0.0008646654,0.00001442276,0.003722415,0.01237126],"study_design_scores_gemma":[0.008795051,0.0004803192,0.6239161,0.002763177,0.0002848065,0.000316988,0.00709729,0.3532151,0.00003158436,0.0001446501,0.002725294,0.0002295631],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827831,0.005797196,0.005994384,0.002762981,0.0009168907,0.001634179,0.00002821008,0.00002469606,0.00005835223],"genre_scores_gemma":[0.9986809,0.0002013444,0.0004932244,0.0001741097,0.0002798972,0.00002811392,0.00002176424,0.0000289025,0.00009173182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3520567,"threshold_uncertainty_score":0.7377933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01803461854946382,"score_gpt":0.3478507290520844,"score_spread":0.3298161105026206,"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."}}