{"id":"W4412163722","doi":"10.1158/1557-3265.aimachine-a015","title":"Abstract A015: Precision medicine approach to melanoma immunotherapy: Predicting response, adverse events, and hospital admissions using machine learning and explainable artificial intelligence","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immunotherapy; Medicine; Adverse effect; Precision medicine; Melanoma; Artificial intelligence; Machine learning; Intensive care medicine; Medical physics; Computer science; Internal medicine; Cancer; Pathology; Cancer research","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004029952,0.0007833327,0.0006571662,0.002692334,0.0004852639,0.001901036,0.0008489763,0.0008645547,0.001907167],"category_scores_gemma":[0.01305928,0.0002553244,0.001372653,0.001825584,0.000413658,0.0008327524,0.000784685,0.00142124,0.0002565724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00181237,"about_ca_system_score_gemma":0.001529531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009995822,"about_ca_topic_score_gemma":0.007402993,"domain_scores_codex":[0.9984004,0.0008759781,0.0001471641,0.0003056308,0.0002094459,0.00006137564],"domain_scores_gemma":[0.9895324,0.007801019,0.001254354,0.0004751478,0.0007613709,0.0001757143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008037703,0.0006311852,0.3940288,0.0007954761,0.001594172,0.0003428911,0.0002748041,0.3831718,0.001306332,0.004709641,0.01245748,0.1998837],"study_design_scores_gemma":[0.00008372003,0.0003676248,0.05532507,0.0001860576,0.0002468858,0.0001453877,0.000133082,0.9247983,0.001203163,0.01415835,0.003305974,0.00004640625],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7257893,0.007262648,0.2251533,0.01843794,0.0003257449,0.0006167908,0.01486731,0.001694793,0.005852294],"genre_scores_gemma":[0.9592515,0.0006604299,0.0339735,0.00049616,0.000180886,0.0001472551,0.004586576,0.00001874307,0.0006849614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009995822,"threshold_uncertainty_score":0.02131271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1251146708859482,"score_gpt":0.5043422509448524,"score_spread":0.3792275800589042,"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."}}