{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002690096,0.0001100342,0.0004985154,0.00004684383,0.00004560631,0.000007668035,0.00007021044,0.00003935933,0.00002614052],"category_scores_gemma":[0.0004784667,0.00007441713,0.00003739421,0.0001832464,0.00006625822,0.00002578529,0.00002941212,0.00009950147,0.000001174825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001411353,"about_ca_system_score_gemma":0.0001119378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007125074,"about_ca_topic_score_gemma":0.001405173,"domain_scores_codex":[0.9990708,0.00008003663,0.0003253183,0.0002288294,0.00008722815,0.000207848],"domain_scores_gemma":[0.9980346,0.001607537,0.0001208478,0.0001296507,0.00004319528,0.00006416063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006996834,0.0006021922,0.9182379,0.0001777589,0.00006087635,0.00002506957,0.003310606,0.0006128372,0.00002428892,0.0002240788,0.0001132019,0.07591148],"study_design_scores_gemma":[0.002965506,0.007171433,0.9732988,0.0003180482,0.0004006271,0.000003384488,0.002536788,0.004118512,0.005717499,0.001935246,0.001352421,0.0001816848],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9658963,0.001342263,0.00004674978,0.03023032,0.00009348912,0.002253853,0.000105149,0.00001790902,0.00001394679],"genre_scores_gemma":[0.9943708,0.001349599,0.0002162746,0.001290368,0.0001998648,0.00254427,0.00001416857,0.0000129908,0.000001657089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0757298,"threshold_uncertainty_score":0.3034642,"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."}}