{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006753561,0.0001834795,0.0004934045,0.0001673541,0.0002291752,0.00001876879,0.00007611411,0.00002048183,0.00005561417],"category_scores_gemma":[0.00006617091,0.0001472168,0.00005207736,0.00008092438,0.0001334453,0.0001194187,0.00002865763,0.0001599539,5.187841e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001935751,"about_ca_system_score_gemma":0.0006880452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002058866,"about_ca_topic_score_gemma":0.0004675748,"domain_scores_codex":[0.9983541,0.000151073,0.0007678466,0.0002380786,0.0003452734,0.0001435647],"domain_scores_gemma":[0.9983818,0.0001371302,0.0007900964,0.0000844034,0.0004704951,0.000136089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002151502,0.0001098196,0.8689622,0.0000931421,0.000905641,0.000001988292,0.002241628,0.004493749,0.003202272,0.00001380018,0.0001726285,0.1176516],"study_design_scores_gemma":[0.01661941,0.00145198,0.9404119,0.0002185344,0.0004297157,0.000008993406,0.0003147357,0.03653558,0.0009372207,0.00001212524,0.002918543,0.0001412556],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743131,0.007274072,0.01456547,0.001240632,0.0002654184,0.0006605888,0.001659131,0.000007645169,0.00001396025],"genre_scores_gemma":[0.9874964,0.003596232,0.008192596,0.0003811854,0.0001231646,0.0001220579,0.00001669435,0.00002567924,0.00004595055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1175103,"threshold_uncertainty_score":0.6003329,"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."}}