{"id":"W4394770131","doi":"10.1200/cci.23.00255","title":"Machine Learning–Based Survival Prediction Models for Progression-Free and Overall Survival in Advanced-Stage Hodgkin Lymphoma","year":2024,"lang":"en","type":"article","venue":"JCO Clinical Cancer Informatics","topic":"Lymphoma Diagnosis and Treatment","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Seagen (Canada)","funders":"National Cancer Institute; Genentech; Bristol-Myers Squibb","keywords":"Stage (stratigraphy); Lymphoma; Overall survival; Progression-free survival; Oncology; Hodgkin lymphoma; Internal medicine; Artificial intelligence; Medicine; Computer science; Biology","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":[0.007880214,0.0007371627,0.0006843279,0.001313888,0.0003482249,0.0008111143,0.0007159294,0.0004978646,0.0008036317],"category_scores_gemma":[0.01509132,0.0002735109,0.0009447667,0.0005223812,0.0004019325,0.0006434611,0.0007044875,0.001013294,0.0002730633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001109292,"about_ca_system_score_gemma":0.001171169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006289276,"about_ca_topic_score_gemma":0.004660666,"domain_scores_codex":[0.9985043,0.0009273308,0.00008212076,0.0001933682,0.0002027846,0.00009008164],"domain_scores_gemma":[0.9922557,0.005911421,0.0006742786,0.0003175196,0.00070547,0.0001356368],"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":[0.0006206519,0.0003433771,0.2844865,0.0000976747,0.0007347166,0.0001394858,0.0001265142,0.6448859,0.0006227317,0.0006682905,0.001036675,0.06623752],"study_design_scores_gemma":[0.00002479102,0.0001816226,0.02628497,0.00002759916,0.00007763604,0.00004760627,0.00002721557,0.971763,0.0003158535,0.001045823,0.0001915555,0.00001224794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9592428,0.001069914,0.03762313,0.0004512308,0.00004393333,0.00007290611,0.0004225755,0.000200279,0.000873345],"genre_scores_gemma":[0.9944398,0.0001213613,0.004595185,0.00003396387,0.00001945035,0.00003562446,0.0004469507,0.000006305085,0.0003013382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007880214,"threshold_uncertainty_score":0.04167503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06404453194239833,"score_gpt":0.3941105043026188,"score_spread":0.3300659723602204,"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."}}