{"id":"W4392826795","doi":"10.1016/j.ijrobp.2024.01.103","title":"Developing Machine Learning Algorithms Incorporating Patient Reported Outcomes to Predict Disease Progression in Head and Neck Cancers","year":2024,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University Health Network; Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Medicine; Head and neck; Head and neck cancer; Disease; Machine learning; Oncology; Algorithm; Artificial intelligence; Internal medicine; Surgery; Cancer","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002330514,0.0005325183,0.0007365324,0.0009565363,0.0002412788,0.0009716029,0.0007117904,0.0007018142,0.0007075223],"category_scores_gemma":[0.007493419,0.0002557127,0.0006386343,0.0005425985,0.0001886652,0.0006259814,0.0003983737,0.00102084,0.0002694763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004829466,"about_ca_system_score_gemma":0.0009799439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003539535,"about_ca_topic_score_gemma":0.003242667,"domain_scores_codex":[0.9996036,0.000150939,0.00005285917,0.00008787219,0.00006367902,0.00004110251],"domain_scores_gemma":[0.9970564,0.002106602,0.0002476177,0.00008334022,0.0004306065,0.00007550387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005565286,0.0008254481,0.1769201,0.0001095384,0.0004697752,0.0001131902,0.00006151776,0.4454138,0.001983178,0.001087423,0.002889592,0.3695699],"study_design_scores_gemma":[0.00002112292,0.0001163696,0.005757271,0.0000119858,0.00005141446,0.00003218426,0.00001491798,0.9919893,0.0008489745,0.0009077041,0.0002419136,0.000006834314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7695037,0.00136152,0.2237325,0.00113821,0.0002176835,0.0001974778,0.001124787,0.0009451007,0.001779047],"genre_scores_gemma":[0.9554198,0.0002710322,0.04195684,0.0001089647,0.00008695711,0.0001122901,0.001219729,0.0000291381,0.0007953259],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003539535,"threshold_uncertainty_score":0.01232505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02007327497365244,"score_gpt":0.3680006166736511,"score_spread":0.3479273416999986,"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."}}