{"id":"W4297925391","doi":"10.48550/arxiv.1703.08516","title":"Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Maisonneuve-Rosemont; Centre Hospitalier Universitaire de Sherbrooke; Jewish General Hospital; Centre Hospitalier de l’Université de Montréal; McGill University","funders":"","keywords":"Radiomics; Medicine; Head and neck cancer; Oncology; Radiation therapy; Head and neck; Internal medicine; Risk assessment; Radiology; Surgery; Computer science","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":[0.008244948,0.000764379,0.0009945859,0.004084641,0.0002682561,0.001065037,0.0007243307,0.0006090832,0.00083498],"category_scores_gemma":[0.01507072,0.0002808663,0.0008381911,0.001281693,0.0006595548,0.0008064651,0.001046966,0.0006458081,0.0004046186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006924896,"about_ca_system_score_gemma":0.0005444358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001412001,"about_ca_topic_score_gemma":0.00129313,"domain_scores_codex":[0.9980397,0.001110171,0.0001289868,0.0002736364,0.0003416673,0.0001058811],"domain_scores_gemma":[0.9956072,0.002420167,0.001053643,0.0003734044,0.0004342404,0.0001112679],"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":[0.001443109,0.0003018133,0.2804383,0.0004184921,0.001017418,0.000402339,0.0003825183,0.2404717,0.01250865,0.007686241,0.00398708,0.4509423],"study_design_scores_gemma":[0.0000484073,0.0003981696,0.09221864,0.0000857444,0.0002078892,0.000600566,0.0001341812,0.8712677,0.008036125,0.02382578,0.003103337,0.00007343556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3576314,0.006315626,0.6288632,0.001763397,0.0001193086,0.0002498759,0.001501002,0.001251089,0.002305111],"genre_scores_gemma":[0.9480488,0.0004517395,0.05008667,0.0000990724,0.00009988181,0.00007877985,0.0006702245,0.00004978144,0.0004149906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008244948,"threshold_uncertainty_score":0.04360396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04985156051663774,"score_gpt":0.2831586493096029,"score_spread":0.2333070887929651,"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."}}