{"id":"W4307041077","doi":"10.1016/j.ejca.2022.10.011","title":"Head and neck cancer predictive risk estimator to determine control and therapeutic outcomes of radiotherapy (HNC-PREDICTOR): development, international multi-institutional validation, and web implementation of clinic-ready model-based risk stratification for head and neck cancer","year":2022,"lang":"en","type":"article","venue":"European Journal of Cancer","topic":"Head and Neck Cancer Studies","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"U.S. National Library of Medicine; National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; National Cancer Institute; National Institute of Dental and Craniofacial Research; University of Texas MD Anderson Cancer Center","keywords":"Medicine; Head and neck cancer; Radiation therapy; Cohort; Stage (stratigraphy); Propensity score matching; Internal medicine; Oncology","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":[0.01020946,0.0007256863,0.001041413,0.001172003,0.0002578439,0.001004885,0.001211564,0.0005827823,0.001634695],"category_scores_gemma":[0.02122111,0.0003389521,0.0009381187,0.0007863662,0.0002580128,0.0005041391,0.00137067,0.001763505,0.0005839254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006993412,"about_ca_system_score_gemma":0.001921505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008699755,"about_ca_topic_score_gemma":0.00582489,"domain_scores_codex":[0.9981968,0.0008811515,0.0000940063,0.000376388,0.0003478527,0.0001038649],"domain_scores_gemma":[0.9928362,0.004281916,0.0007404173,0.0007779074,0.001078235,0.0002853882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002310509,0.000965751,0.5960898,0.0002998352,0.001873845,0.0001584309,0.0001869471,0.1657933,0.002098689,0.001686101,0.02150282,0.207034],"study_design_scores_gemma":[0.0002574416,0.0004878018,0.08205187,0.00007798779,0.0002619597,0.0001725046,0.00005421814,0.9102303,0.002003622,0.001920015,0.002443872,0.00003841673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6903571,0.001399215,0.2796072,0.001398641,0.0002602077,0.0009946602,0.01621714,0.006785403,0.002980332],"genre_scores_gemma":[0.8883113,0.000205344,0.09292474,0.0002869859,0.00006312546,0.0005942119,0.01664814,0.0002131066,0.0007529863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01020946,"threshold_uncertainty_score":0.0539934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05362125233116699,"score_gpt":0.3936935447252118,"score_spread":0.3400722923940448,"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."}}