{"id":"W3048549272","doi":"10.1007/s10439-020-02585-y","title":"Stochastic Sequential Modeling: Toward Improved Prostate Cancer Diagnosis Through Temporal-Ultrasound","year":2020,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Robarts Clinical Trials; London Health Sciences Centre; University of British Columbia; Queen's University","funders":"U.S. National Library of Medicine; Natural Sciences and Engineering Research Council of Canada; National Science Foundation of Sri Lanka; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Science Foundation; National Institutes of Health; National Institute of General Medical Sciences; California HIV/AIDS Research Program","keywords":"Malignancy; Prostate cancer; Computer science; Artificial intelligence; Cancer; Ultrasound; Hidden Markov model; Pattern recognition (psychology); Medicine; Radiology; Pathology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001625729,0.0001832476,0.0002605439,0.00006907621,0.00003664077,0.00005093717,0.0005166238,0.0001016828,0.00002682046],"category_scores_gemma":[0.000224472,0.0001783089,0.00010275,0.0006131272,0.00005538887,0.0004408251,0.0001779901,0.0002201693,0.000005225766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000350587,"about_ca_system_score_gemma":0.000116407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002407946,"about_ca_topic_score_gemma":0.000001259999,"domain_scores_codex":[0.9984208,0.00001685794,0.0003848891,0.0004001001,0.0004109534,0.0003663647],"domain_scores_gemma":[0.9992419,0.00008359043,0.0000955744,0.0002233592,0.000110628,0.0002449016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001325854,0.0002611354,0.0001432004,0.001305222,0.0005574351,0.00004879804,0.01278739,0.7762266,0.1115184,0.002487504,0.006784675,0.0877471],"study_design_scores_gemma":[0.0002564433,0.0002007092,0.00001010005,0.00007223238,0.000009527239,0.000003593399,0.0000236599,0.9795015,0.01681264,0.0002900912,0.002610053,0.0002093938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01345253,0.0007304059,0.9751661,0.009563718,0.0006035097,0.0001882135,0.00002799471,0.0002605762,0.000006905232],"genre_scores_gemma":[0.9834042,0.0003069234,0.01509318,0.0006727049,0.0003380824,0.0001527107,0.000006429409,0.00002251357,0.000003237243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9699517,"threshold_uncertainty_score":0.7271226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07263612052667848,"score_gpt":0.3043334567195237,"score_spread":0.2316973361928452,"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."}}