{"id":"W4417520722","doi":"10.1016/j.ejmp.2025.105693","title":"Fast personalized CT dose calculations with GPUMCD","year":2025,"lang":"en","type":"article","venue":"Physica Medica","topic":"Radiation Dose and Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut universitaire de cardiologie et de pneumologie de Québec; Centre hospitalier de l'Université Laval; Université Laval","funders":"Canadian Cancer Society; McGill University Health Centre; Québec Consortium for Drug Discovery; Fonds de Recherche du Québec - Santé; Fondation Institut Universitaire de Cardiologie et de Pneumologie de Québec; Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval; AstraZeneca; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec; Pfizer","keywords":"Dosimetry; Pipeline (software); Medical imaging; Computed tomography; Radiation dose","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":[0.000375968,0.0006951278,0.0005110664,0.0003944366,0.0002837367,0.0009050686,0.001391836,0.0007139858,0.00439801],"category_scores_gemma":[0.001542218,0.0004612135,0.0007028312,0.0004403947,0.0002746884,0.0004341527,0.000729421,0.0008632113,0.0009903611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001130432,"about_ca_system_score_gemma":0.001110434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00806835,"about_ca_topic_score_gemma":0.00778865,"domain_scores_codex":[0.9997332,0.00004346941,0.00001599666,0.00004851611,0.0001396821,0.00001921705],"domain_scores_gemma":[0.9995477,0.0001789631,0.00003123002,0.00009499802,0.0001242949,0.00002287804],"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":[0.000248185,0.00009119519,0.003289012,0.0002294747,0.00012791,0.0001983651,0.0002547532,0.8251413,0.02244688,0.004704684,0.008368885,0.1348993],"study_design_scores_gemma":[0.00001479392,0.00001377174,0.000282459,0.000007442428,0.000008391457,0.00003725827,0.000005780293,0.9889172,0.005723036,0.0008072791,0.004174144,0.000008397694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03329803,0.0003088975,0.9426425,0.0002270417,0.00007797522,0.0001126221,0.0006452114,0.01669353,0.005994205],"genre_scores_gemma":[0.430557,0.0002305743,0.5608625,0.0002339773,0.00002634009,0.0002520818,0.001367694,0.002618077,0.003851727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00806835,"threshold_uncertainty_score":0.01604277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01017260486949229,"score_gpt":0.2930325985749444,"score_spread":0.2828599937054521,"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."}}