{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.008269235,0.0001642203,0.0003091211,0.0002658682,0.001456301,0.00268805,0.005208912,0.00003038902,0.00002123647],"category_scores_gemma":[0.0004043923,0.00008654899,0.00002061155,0.002398032,0.001038313,0.003308035,0.002692216,0.0002435328,0.000001948893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000314817,"about_ca_system_score_gemma":0.0003193684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003732977,"about_ca_topic_score_gemma":0.0002410658,"domain_scores_codex":[0.9969363,0.0002537567,0.0003668352,0.00100099,0.0009641102,0.0004779789],"domain_scores_gemma":[0.9966519,0.0003803561,0.0002694268,0.002115502,0.0002784301,0.0003043922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001092349,0.00008961862,0.02095285,0.00002590356,0.001209779,0.00004732599,0.0007872939,0.000136281,0.0003431351,0.1153117,0.009287972,0.8516989],"study_design_scores_gemma":[0.0001599877,0.0001490614,0.01471946,0.00004622477,0.0007983599,0.00009477927,0.0001967547,0.9808452,0.000008775661,0.001224424,0.001565898,0.0001911274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003622751,0.0001898434,0.9941517,0.000922389,0.0004934278,0.00007640523,0.0004406933,0.000019996,0.00008275997],"genre_scores_gemma":[0.2672927,0.00007852427,0.7320957,0.0001500746,0.0003221767,0.000002686794,0.00004061478,0.000005178605,0.00001232275],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9807088,"threshold_uncertainty_score":0.9998437,"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."}}