{"id":"W4399826484","doi":"10.1177/10732748241264704","title":"Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence","year":2024,"lang":"en","type":"article","venue":"Cancer Control","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Toronto Metropolitan University","keywords":"Medicine; Artificial intelligence; Context (archaeology); Generative grammar; Disease; Machine learning; Computer science; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0003315051,0.00009671697,0.0002322838,0.00007116627,0.00002766857,0.00002422502,0.0001056659,0.00003832065,0.0001967761],"category_scores_gemma":[0.00005993251,0.00005675975,0.0000756449,0.000228716,0.0001435215,0.00003435349,0.000006462279,0.0003856358,0.000003745106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007112955,"about_ca_system_score_gemma":0.0002077776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006986071,"about_ca_topic_score_gemma":0.0001189808,"domain_scores_codex":[0.9991711,0.00008324711,0.000214482,0.0001803663,0.0001909315,0.0001599241],"domain_scores_gemma":[0.99958,0.0001850805,0.00003376274,0.0001214951,0.0000458986,0.00003374166],"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":[0.0003671172,0.00007784367,0.00269965,0.00005701496,0.0002316382,0.0002604991,0.006937941,0.002525289,0.02575655,0.01177161,0.001427653,0.9478872],"study_design_scores_gemma":[0.002419565,0.001085454,0.01637156,0.001746671,0.0003461879,0.00004168665,0.002340038,0.8924396,0.01082374,0.01277509,0.05912191,0.0004884746],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5144647,0.136281,0.2101263,0.128282,0.003025772,0.002600361,0.00009495147,0.0001349264,0.004990031],"genre_scores_gemma":[0.9932694,0.001679442,0.0001279522,0.00395854,0.0006391478,0.000183358,0.000002947014,0.00001372503,0.0001254518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9473987,"threshold_uncertainty_score":0.2314595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04148395327002339,"score_gpt":0.3592394429347402,"score_spread":0.3177554896647168,"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."}}