{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006148531,0.00009997078,0.0002045631,0.00009299404,0.0001002171,0.00001561489,0.00006449862,0.00001427243,0.0002819675],"category_scores_gemma":[0.00006974412,0.00007370095,0.00006536111,0.0003789177,0.0001318483,0.00008362975,0.00001630123,0.0001491841,0.00008535817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004439622,"about_ca_system_score_gemma":0.0002565664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003860896,"about_ca_topic_score_gemma":0.000002591301,"domain_scores_codex":[0.9992141,0.00002137067,0.0001270621,0.000187271,0.0002815028,0.0001687429],"domain_scores_gemma":[0.9995027,0.00006033458,0.00003967229,0.0002295329,0.00004969039,0.0001180855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007947563,0.001999296,0.04490669,0.0005675213,0.002174428,0.0003945048,0.006842439,0.0001209708,0.02668235,0.05379061,0.3978174,0.463909],"study_design_scores_gemma":[0.009882092,0.0002188879,0.1544868,0.0007788742,0.0007779095,0.00007075389,0.001143213,0.01714151,0.005489625,0.0006159921,0.8090152,0.0003791679],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5561443,0.0007646636,0.008363459,0.09403372,0.000362596,0.0009094025,0.00001690735,0.0004033298,0.3390016],"genre_scores_gemma":[0.9871,0.0000227268,0.0004538568,0.002442031,0.0002250271,0.00003363271,0.00004453554,0.00001181331,0.009666391],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4635299,"threshold_uncertainty_score":0.3087347,"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."}}