{"id":"W2552179694","doi":"10.5489/cuaj.3896","title":"Relationship between Gleason score and apparent diffusion coefficients of diffusion-weighted magnetic resonance imaging in prostate cancer patients","year":2016,"lang":"en","type":"article","venue":"Canadian Urological Association Journal","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Effective diffusion coefficient; Prostate cancer; Prostatectomy; Medicine; Magnetic resonance imaging; Cutoff; Receiver operating characteristic; Diffusion MRI; Nuclear medicine; Pathological; Prostate; Cancer; Urology; Radiology; Internal medicine","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":[0.0004900853,0.0002760268,0.0001999368,0.0006409819,0.000178567,0.0003513545,0.0001755593,0.000340976,0.001566937],"category_scores_gemma":[0.002961266,0.0001375791,0.0002093313,0.000624455,0.0001943893,0.0003026749,0.0002777194,0.0003827968,0.0002035616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001788952,"about_ca_system_score_gemma":0.0002383501,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001278116,"about_ca_topic_score_gemma":0.001897621,"domain_scores_codex":[0.9997953,0.00005553808,0.00002496603,0.00003118673,0.00005964563,0.0000335436],"domain_scores_gemma":[0.9986029,0.0004041367,0.0005212594,0.00005117065,0.000182975,0.0002375361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005831584,0.000007980973,0.9990115,0.000004409888,0.00002809544,0.00002140587,0.000008246372,0.00006202955,0.00009816377,0.000005292112,0.00002942052,0.0006650977],"study_design_scores_gemma":[0.000007106142,0.00009599002,0.9989709,0.000003308598,0.00002750926,0.0002776875,0.00003120587,0.000355733,0.0000889578,0.00003239455,0.0001054654,0.000003688112],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990777,0.0003834994,0.0001126632,0.00004179871,0.000004745436,0.000004110301,0.0001316666,0.000004957893,0.0002388924],"genre_scores_gemma":[0.9997009,0.00004977338,0.00006104769,0.000009017304,0.000005404217,0.000001875252,0.0001151926,7.496378e-7,0.00005617865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9987219,"threshold_uncertainty_score":0.00524193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01530991981328146,"score_gpt":0.2454699192838168,"score_spread":0.2301599994705354,"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."}}