{"id":"W4412163851","doi":"10.1158/1557-3265.aimachine-a011","title":"Abstract A011: Interpretable machine learning for discovery, evaluation and clinical translation of context-specific dependencies","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Inro Consultants (Canada); Roche (Canada)","funders":"","keywords":"Context (archaeology); Translation (biology); Artificial intelligence; Computer science; Natural language processing; Machine learning; Medicine; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01901722,0.0001297198,0.0004413079,0.0002209481,0.0002857339,0.0001957756,0.0007284879,0.0002145066,0.00005912495],"category_scores_gemma":[0.005140445,0.0001155606,0.0001802596,0.000476503,0.0003811008,0.0004564238,0.0002888434,0.001499831,0.000004171611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001044025,"about_ca_system_score_gemma":0.0009632042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001121556,"about_ca_topic_score_gemma":0.001018879,"domain_scores_codex":[0.995125,0.001700373,0.001220984,0.0007488192,0.000807478,0.0003973393],"domain_scores_gemma":[0.9884041,0.009685998,0.0002219704,0.0004978768,0.001065135,0.0001248995],"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":[0.0003200148,0.00008817175,0.1913031,0.0002006331,0.00004101465,7.348826e-7,0.0002035597,0.0003023199,0.00005139735,0.005074159,0.0004546022,0.8019603],"study_design_scores_gemma":[0.002410743,0.0007818764,0.3498401,0.0004542697,0.0000247937,8.023171e-7,0.000138294,0.610658,0.0001138868,0.006452467,0.02894925,0.0001755289],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6471069,0.03934763,0.2862429,0.01808985,0.002429152,0.003652574,0.00003996437,0.0001548394,0.002936265],"genre_scores_gemma":[0.9941756,0.002401632,0.002524459,0.0001230451,0.0001527518,0.0001430296,0.00001199925,0.00001242792,0.0004550995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8017848,"threshold_uncertainty_score":0.6591027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3346744998298291,"score_gpt":0.5760950646150794,"score_spread":0.2414205647852504,"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."}}