{"id":"W4365503963","doi":"10.1088/1361-6560/accd42","title":"Fast <i>D</i> <sub>M,M</sub> calculation in LDR brachytherapy using deep learning methods","year":2023,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital; Université Laval; Centre hospitalier de l'Université Laval","funders":"Canada Research Chairs; Centre Hospitalier Universitaire de Québec; Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Fonds de recherche du Québec – Nature et technologies; Université Laval","keywords":"Brachytherapy; Nuclear medicine; Monte Carlo method; Prostate brachytherapy; Kernel (algebra); Radiation treatment planning; Computation; Computer science; Mathematics; Algorithm; Medicine; Radiation therapy; Statistics; Radiology","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":[0.0003873784,0.0004317019,0.000274778,0.0002743037,0.0001891279,0.0005277568,0.0006052234,0.0004954462,0.001823824],"category_scores_gemma":[0.001263502,0.0002784453,0.0003158073,0.0002882011,0.0002655468,0.000385293,0.0004120572,0.0005552722,0.0003873537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001143621,"about_ca_system_score_gemma":0.0007784702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008398153,"about_ca_topic_score_gemma":0.007000387,"domain_scores_codex":[0.9999152,0.00002462922,0.000004469583,0.00001464345,0.00002895411,0.00001213507],"domain_scores_gemma":[0.9996818,0.0001794288,0.00003300938,0.0000227978,0.00006437248,0.00001858869],"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":[0.00007628374,0.00003510252,0.001396652,0.00006967162,0.00002750395,0.00005939912,0.00002935695,0.9386525,0.006124504,0.001523965,0.0008892607,0.05111577],"study_design_scores_gemma":[0.000001838945,0.000005615978,0.0001088831,0.000002568496,0.000001595897,0.000004472105,0.000001740829,0.9982225,0.001211707,0.0002928404,0.000144669,0.000001495758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1181425,0.0005525638,0.8726637,0.0003838969,0.00003474547,0.00006495923,0.0002149801,0.002621001,0.005321564],"genre_scores_gemma":[0.8625442,0.0001919384,0.1334619,0.0001490368,0.00001211517,0.00008445293,0.0002120209,0.000237171,0.003107148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008398153,"threshold_uncertainty_score":0.01669854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1247372471291116,"score_gpt":0.4504936123756737,"score_spread":0.3257563652465621,"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."}}