{"id":"W3127425164","doi":"10.1158/1557-3265.endomet20-ia017","title":"Abstract IA017: Molecular classification and stratification: Diving deeper","year":2021,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Categorization; Homogeneous; Risk stratification; Stratification (seeds); Context (archaeology); Endometrial cancer; Clinical Practice; Computational biology; Cancer; Medicine; Bioinformatics; Oncology; Biology; Computer science; Internal medicine; Artificial intelligence; Family 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":[0.04499402,0.001202765,0.002092776,0.008185115,0.002105973,0.0123226,0.003468575,0.003372893,0.02091741],"category_scores_gemma":[0.1426418,0.001081071,0.002203237,0.006333671,0.006225312,0.0134568,0.007288536,0.01492821,0.01098816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004983253,"about_ca_system_score_gemma":0.01212998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007951227,"about_ca_topic_score_gemma":0.008150849,"domain_scores_codex":[0.984292,0.008624586,0.001895651,0.001145895,0.003469713,0.000572064],"domain_scores_gemma":[0.9092184,0.03264161,0.006449364,0.0112469,0.0343422,0.006101548],"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":[0.0002473231,0.00005002539,0.007643334,0.001064095,0.0001773475,0.0001326328,0.0007809252,0.0007784885,0.0007315956,0.03975254,0.548315,0.4003268],"study_design_scores_gemma":[0.0001283421,0.0002256438,0.01441619,0.009678507,0.0003486742,0.0007676446,0.00195684,0.004819649,0.0008605464,0.3283273,0.6382558,0.0002148887],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.005297024,0.04255959,0.04859575,0.8478009,0.02822739,0.0003204446,0.003135233,0.001161386,0.02290237],"genre_scores_gemma":[0.1829822,0.1110345,0.2440716,0.2930271,0.1042498,0.00126844,0.01382439,0.003259774,0.04628219],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04499402,"threshold_uncertainty_score":0.237954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1870674673411039,"score_gpt":0.4963170204378261,"score_spread":0.3092495530967222,"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."}}