{"id":"W4417070269","doi":"10.1016/j.engmed.2025.100116","title":"Clinical evaluation of GenAI adaptive cancer therapy","year":2025,"lang":"en","type":"article","venue":"EngMedicine","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Zoo","funders":"Toronto Metropolitan University","keywords":"Clinical trial; Cancer; Precision medicine; MEDLINE; Cancer therapy; Predictive power; Cancer treatment; Clinical research","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008755012,0.0003837157,0.0008483362,0.0005675258,0.0003158772,0.001609707,0.0007184008,0.000951048,0.004674746],"category_scores_gemma":[0.02351906,0.0001299153,0.000658693,0.0005570377,0.0009164364,0.0007663125,0.0009335472,0.001397209,0.001295055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077062,"about_ca_system_score_gemma":0.001066371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005267378,"about_ca_topic_score_gemma":0.0006457221,"domain_scores_codex":[0.995181,0.003392078,0.000274437,0.0003306073,0.0007176442,0.000104202],"domain_scores_gemma":[0.9909422,0.004923476,0.001070263,0.0008692553,0.001725033,0.0004698027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","study_design_scores_codex":[0.01588885,0.00182053,0.03213254,0.001655729,0.0007334814,0.000367617,0.0005715073,0.01655265,0.006946725,0.005912238,0.02505459,0.8923635],"study_design_scores_gemma":[0.01163762,0.1819214,0.1444995,0.004704527,0.003539933,0.005878511,0.001756606,0.1387072,0.0592632,0.03558408,0.4118829,0.0006244667],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6275287,0.06483442,0.1493505,0.02758833,0.003062293,0.006718763,0.007368997,0.003274023,0.1102741],"genre_scores_gemma":[0.9405593,0.007500301,0.03684764,0.00576386,0.0004080824,0.00232069,0.002630725,0.0001745438,0.003794841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008755012,"threshold_uncertainty_score":0.04630148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5309568333701008,"score_gpt":0.6220619598655507,"score_spread":0.09110512649544988,"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."}}