{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003683813,0.000998111,0.0006651883,0.0009982004,0.0002385774,0.001057771,0.001193785,0.0008853189,0.00525078],"category_scores_gemma":[0.01329707,0.0003552477,0.001009669,0.0005128151,0.0005183116,0.0006741342,0.001128935,0.001511746,0.0008576449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007001093,"about_ca_system_score_gemma":0.001018365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002221416,"about_ca_topic_score_gemma":0.00268941,"domain_scores_codex":[0.9986518,0.0007850907,0.00006518659,0.000239335,0.0002096825,0.00004889426],"domain_scores_gemma":[0.9951533,0.003552438,0.0003002867,0.0004460252,0.0003941827,0.0001537204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001291166,0.0003157655,0.01222325,0.0004337689,0.0003915865,0.0004852198,0.0001006134,0.6753435,0.008158025,0.01479886,0.02183823,0.26462],"study_design_scores_gemma":[0.00003872743,0.00009110998,0.0003961069,0.00001717261,0.00001755026,0.0000528129,0.00000522731,0.9872398,0.001482292,0.009621684,0.001028832,0.000008721964],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.0444478,0.0006707535,0.9378936,0.001288419,0.0001460564,0.0001899088,0.002637899,0.01116521,0.001560422],"genre_scores_gemma":[0.6432662,0.0004137584,0.3485544,0.0004773974,0.0001094052,0.0004863513,0.004036257,0.0009364077,0.001719775],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.00525078,"threshold_uncertainty_score":0.01948214,"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."}}