{"id":"W4245064891","doi":"10.1002/9780471420194.tnmm06","title":"Cancer Informatics","year":2017,"lang":"en","type":"other","venue":"TNM Online","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Informatics; Health informatics; Computer science; Best practice; Data science; Health informatics tools; Knowledge management; Health Administration Informatics; Process (computing); Translational research informatics; Cancer; Frontier; Health care; Medicine; Engineering; Political science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004354403,0.0001562613,0.0001828156,0.0001279222,0.00005287511,0.00008398735,0.001176476,0.0001777365,0.001455438],"category_scores_gemma":[0.00001644854,0.0001428935,0.00004355473,0.00007174178,0.0000383793,0.0001474528,0.0002467897,0.0001953306,0.0002522517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008396475,"about_ca_system_score_gemma":0.000165187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003947697,"about_ca_topic_score_gemma":0.001461806,"domain_scores_codex":[0.9993287,0.000008333176,0.0001309367,0.0001494297,0.0002141371,0.0001684931],"domain_scores_gemma":[0.9984909,0.00001048253,0.0003779018,0.001042431,0.0000281832,0.00005008734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[3.739025e-7,0.000007319453,0.000003564771,0.00003326875,0.00001208753,0.000001461323,0.00004407188,0.000003276146,9.213988e-7,0.00008395918,0.8196766,0.1801331],"study_design_scores_gemma":[0.0001013366,0.0000159859,0.00002646131,0.0002467176,0.000006682625,0.000003503895,0.000001644845,0.005551517,0.00001649743,0.00008944544,0.9937712,0.0001690312],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000001764727,0.001028516,0.1495623,0.0004754122,0.003374577,0.0002037392,0.0008355926,0.0008262248,0.8436919],"genre_scores_gemma":[0.00001054936,0.0009251723,0.05349649,0.0003603908,0.001341123,0.00002559285,0.00006137837,0.0001160071,0.9436633],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1799641,"threshold_uncertainty_score":0.9994574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02289943416747525,"score_gpt":0.3145312743595317,"score_spread":0.2916318401920564,"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."}}