{"id":"W2901051058","doi":"10.1080/09553002.2019.1589013","title":"Proliferation saturation index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses","year":2019,"lang":"en","type":"article","venue":"International Journal of Radiation Biology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre","funders":"National Cancer Institute; National Institutes of Health; Moffitt Cancer Center","keywords":"Radiation therapy; Bayesian probability; Computer science; Identifiability; Medicine; Statistics; Nuclear medicine; Medical physics; Mathematics; Artificial intelligence; Radiology; Machine learning","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":[0.0005108171,0.0001058755,0.0002392259,0.0006887667,0.00002011002,0.00003915199,0.0001640652,0.0001061252,0.00005934649],"category_scores_gemma":[0.0002260563,0.00008740342,0.00006340156,0.0001557983,0.00003144021,0.0002715727,0.0000125502,0.0003436594,0.000005288618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002949555,"about_ca_system_score_gemma":0.000147819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001050498,"about_ca_topic_score_gemma":0.000001625332,"domain_scores_codex":[0.9985985,0.0002336303,0.0005255964,0.0001879577,0.0003230043,0.0001312686],"domain_scores_gemma":[0.9990355,0.00009725158,0.0003184821,0.0001102,0.0003129033,0.0001256845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006414128,0.0004409625,0.6812484,0.000006914114,0.0001501886,0.00002600485,0.002218405,0.009970147,0.03192893,0.003049187,0.0006916615,0.263855],"study_design_scores_gemma":[0.007017558,0.004536356,0.7123517,0.0001457252,0.00001952295,0.0007987742,0.0004889764,0.2417126,0.001401605,0.0005616287,0.03069373,0.0002717635],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.902617,0.0002465184,0.09227002,0.003117873,0.001014649,0.0003504588,0.000005893321,0.0000125253,0.0003650452],"genre_scores_gemma":[0.9896598,0.0001312732,0.007995483,0.0013949,0.000683746,0.000008063621,0.00007076701,0.00001429159,0.00004170771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2635833,"threshold_uncertainty_score":0.3564208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0114838012462086,"score_gpt":0.3030673586487815,"score_spread":0.2915835574025729,"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."}}