{"id":"W2999399991","doi":"10.1016/s1470-2045(19)30738-7","title":"Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study","year":2020,"lang":"en","type":"article","venue":"The Lancet Oncology","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":615,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Toronto General Hospital; University Health Network","funders":"EIT Health; European Research Council; Walter Ahlströmin Säätiö; KAUTE-Säätiö; Syöpäjärjestöt; Forskningsrådet om Hälsa, Arbetsliv och Välfärd; Vetenskapsrådet; Emil Aaltosen Säätiö; Orionin Tutkimussäätiö; Tekniikan Edistämissäätiö; Tampereen Yliopisto; Tampereen Teknillinen Yliopisto; Cancerfonden; Academy of Finland","keywords":"Medicine; Grading (engineering); Prostate cancer; Concordance; Prostate; Receiver operating characteristic; Population; Biopsy; Radiology; Pathology; Cancer; 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":[0.006613266,0.0005521002,0.000541592,0.001319916,0.0005780975,0.001319899,0.001067857,0.001135324,0.00130563],"category_scores_gemma":[0.03315432,0.0003990204,0.001210238,0.0009193548,0.001117628,0.001011739,0.0007919831,0.001190331,0.0003281203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005553945,"about_ca_system_score_gemma":0.0005127806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004228354,"about_ca_topic_score_gemma":0.003279382,"domain_scores_codex":[0.9960076,0.002682742,0.0001655725,0.0004482546,0.0005650024,0.0001308989],"domain_scores_gemma":[0.9831593,0.01257013,0.001443897,0.001345895,0.001092484,0.0003882937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004073799,0.002016646,0.9597245,0.0001429674,0.001641061,0.0001815763,0.0006771876,0.00114864,0.0005652072,0.0002384164,0.0007812071,0.02880888],"study_design_scores_gemma":[0.000369535,0.005314212,0.9786938,0.00004987316,0.001417996,0.001248019,0.00115351,0.00931101,0.0004928994,0.0009229102,0.0009736363,0.0000526125],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975715,0.000630318,0.000864116,0.0001259505,0.00001844584,0.00004563083,0.0001903262,0.000009474472,0.000544211],"genre_scores_gemma":[0.9985731,0.0002828096,0.0005542709,0.00006009162,0.00002473622,0.00002454873,0.0002357693,0.000004333021,0.0002403094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006613266,"threshold_uncertainty_score":0.03497475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1147713191908764,"score_gpt":0.3926586424228578,"score_spread":0.2778873232319813,"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."}}