{"id":"W4395659524","doi":"10.1088/1361-6560/ad4448","title":"Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization","year":2024,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital; McGill University; University of Calgary; McGill University Health Centre; Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Canadian Institutes of Health Research; Alliance de recherche numérique du Canada","keywords":"Bayesian optimization; Brachytherapy; Bayesian probability; Dose rate; Computer science; Medical physics; Radiation therapy; Medicine; Radiology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002980245,0.0009405006,0.0008631336,0.0008093764,0.0003619624,0.0007604568,0.000943514,0.0008895403,0.001214055],"category_scores_gemma":[0.003935637,0.0008605968,0.0009582767,0.0005571757,0.0005618486,0.0006679845,0.001003836,0.0009427682,0.0002057165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00107395,"about_ca_system_score_gemma":0.001350739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005384039,"about_ca_topic_score_gemma":0.005273729,"domain_scores_codex":[0.9988832,0.0006215769,0.00003733233,0.0001034992,0.0002758288,0.00007860552],"domain_scores_gemma":[0.9983042,0.001168882,0.0001809063,0.00005521778,0.0002372005,0.00005351737],"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":[0.00004508921,0.00002951675,0.0004174483,0.00004274974,0.00002728825,0.00001438523,0.00002329696,0.9845649,0.002252416,0.00109793,0.0001222105,0.01136271],"study_design_scores_gemma":[0.00001011387,0.00002716076,0.0001602386,0.000005231902,0.000004926681,0.000004945035,0.00000364199,0.9983796,0.000771781,0.0004956215,0.0001320332,0.00000474986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04688471,0.0002456882,0.9505607,0.0001087553,0.00001541214,0.00009139341,0.00003808246,0.0003016286,0.001753633],"genre_scores_gemma":[0.5685778,0.0001506942,0.4289914,0.0001256875,0.00001606312,0.000279492,0.0001254631,0.0002297742,0.001503655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005384039,"threshold_uncertainty_score":0.01576126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.074709557198977,"score_gpt":0.3888552283132533,"score_spread":0.3141456711142763,"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."}}