{"id":"W2790407909","doi":"10.1002/sam.11373","title":"Building cancer prognosis systems with survival function clusters","year":2018,"lang":"en","type":"article","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Homogeneous; Cluster analysis; Computer science; Cancer; Lung cancer; Cluster (spacecraft); Population; Medicine; Covariate; Demographics; Survival analysis; Data mining; Oncology; Internal medicine; Artificial intelligence; Mathematics; Machine learning; Demography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002925085,0.001036094,0.001067266,0.002826012,0.0009778735,0.001751034,0.001386863,0.001077282,0.002160436],"category_scores_gemma":[0.0115844,0.0008904451,0.001862038,0.002144733,0.0005433974,0.001857139,0.001796001,0.001270246,0.001043767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001919385,"about_ca_system_score_gemma":0.002052768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02074963,"about_ca_topic_score_gemma":0.0187603,"domain_scores_codex":[0.9984657,0.0004334353,0.0001520626,0.000570151,0.0002763759,0.0001022963],"domain_scores_gemma":[0.9959746,0.002176034,0.000451906,0.0004603275,0.0008205815,0.0001166354],"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":[0.0002960109,0.000182826,0.02533644,0.0001969813,0.000375052,0.0001882048,0.0005229429,0.7562686,0.00151226,0.01438981,0.006783898,0.1939469],"study_design_scores_gemma":[0.00001263033,0.00002798267,0.001352703,0.00002016799,0.00003776763,0.00002992715,0.00005798997,0.9808528,0.0006288691,0.01539172,0.001572113,0.00001541893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05409454,0.0003149386,0.9358966,0.0006198825,0.00004970049,0.0003427357,0.002563152,0.004890005,0.001228369],"genre_scores_gemma":[0.3902527,0.0002849891,0.600613,0.0001805639,0.00005897806,0.000619005,0.006516232,0.0002247246,0.001249741],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02074963,"threshold_uncertainty_score":0.04125768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08062547735272997,"score_gpt":0.3739859447699232,"score_spread":0.2933604674171932,"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."}}