{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009773514,0.0002626578,0.0006065837,0.0001330228,0.000078875,0.0000866144,0.0001120951,0.000223725,0.00003620911],"category_scores_gemma":[0.0004279004,0.0001944371,0.000207504,0.0001828134,0.00008282162,0.0003784657,0.00009767042,0.0002467077,0.000005978967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003207515,"about_ca_system_score_gemma":0.0004752848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001295343,"about_ca_topic_score_gemma":0.0001630159,"domain_scores_codex":[0.9976581,0.00006598009,0.00117517,0.000276995,0.0004718681,0.0003519065],"domain_scores_gemma":[0.998197,0.0009436948,0.0001897598,0.0003335377,0.0001067104,0.0002293302],"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":[0.002668589,0.001192018,0.4650123,0.002687253,0.0002989215,0.0000686024,0.000802471,0.01485615,0.000003383158,0.003359623,0.001964209,0.5070865],"study_design_scores_gemma":[0.01646651,0.001637946,0.06476943,0.001095041,0.0002071024,0.000005749096,0.0001565526,0.8641627,0.00004903494,0.0007024794,0.05050448,0.0002429297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9670596,0.007993396,0.008452784,0.003535996,0.003290555,0.003524106,0.001663506,0.0005465871,0.003933436],"genre_scores_gemma":[0.9864163,0.00409665,0.006621586,0.0007429006,0.0003634381,0.0007192574,0.0003727073,0.00005399843,0.0006131527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8493066,"threshold_uncertainty_score":0.7928913,"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."}}