{"id":"W3025592469","doi":"10.1016/j.ijrobp.2020.04.045","title":"RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy Via Deep Learning","year":2020,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre; Jewish General Hospital; Mila - Quebec Artificial Intelligence Institute; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Canada Foundation for Innovation; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Brachytherapy; Radiation; Radiation dose; Medical physics; Radiochemistry; Nuclear medicine; Nuclear engineering; Medicine; Radiation therapy; Physics; Chemistry; Radiology; Nuclear physics; Engineering","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":[0.0009752403,0.001498681,0.0009601488,0.0007517529,0.0005086685,0.001958448,0.003417601,0.001779232,0.02846192],"category_scores_gemma":[0.003177231,0.001412699,0.001130664,0.0006665317,0.0003619655,0.001205412,0.002117296,0.002914356,0.005676353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001943024,"about_ca_system_score_gemma":0.002566933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01377647,"about_ca_topic_score_gemma":0.02705451,"domain_scores_codex":[0.9996011,0.000077975,0.00002373409,0.00007116992,0.000171813,0.00005431015],"domain_scores_gemma":[0.9992815,0.0003659403,0.00005492754,0.00009935512,0.0001308595,0.00006738147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009752511,0.00034019,0.002337647,0.001179426,0.0005507608,0.0003884065,0.0002053788,0.3818469,0.01026586,0.008288037,0.1643734,0.4292487],"study_design_scores_gemma":[0.0001371666,0.00006461841,0.0003204408,0.00005825729,0.00002771292,0.00008466192,0.0000220301,0.9729058,0.008413368,0.003840511,0.01408231,0.00004314303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0231162,0.002151382,0.7629153,0.001418183,0.0005691325,0.0004823913,0.009404933,0.1892031,0.01073929],"genre_scores_gemma":[0.2867113,0.0009725871,0.6470419,0.001851682,0.0001330875,0.0008357087,0.01302091,0.02503457,0.0243983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02846192,"threshold_uncertainty_score":0.09521455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01574208353411823,"score_gpt":0.3265058821215922,"score_spread":0.310763798587474,"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."}}