{"id":"W2020774554","doi":"10.1118/1.3181890","title":"SU‐FF‐T‐408: Tissue Inhomogeneities in Monte Carlo Treatment Planning for Proton Therapy","year":2009,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Maisonneuve-Rosemont","funders":"","keywords":"Proton therapy; Monte Carlo method; Imaging phantom; Voxel; Hounsfield scale; Nuclear medicine; Materials science; Artifact (error); Segmentation; Proton; Streaking; Radiation treatment planning; Physics; Energy (signal processing); Biomedical engineering; Beam (structure); Optics; Computer science; Artificial intelligence; Mathematics; Computed tomography; Medicine; Radiology; Radiation therapy; Nuclear physics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00005819277,0.0001580091,0.0002059834,0.00002519605,0.0000421016,0.00001468428,0.00009278894,0.00006016132,0.000009321897],"category_scores_gemma":[0.00001341974,0.0001356732,0.00004436924,0.00008921928,0.00002726382,0.0001075842,0.000005646968,0.0001231776,0.000003519784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000843365,"about_ca_system_score_gemma":0.00002437798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001190396,"about_ca_topic_score_gemma":0.000003388827,"domain_scores_codex":[0.9992428,0.000008932244,0.0001598415,0.0001340384,0.0001753704,0.0002790266],"domain_scores_gemma":[0.9997208,0.00004735192,0.00001645121,0.0001206041,0.00001256075,0.00008219093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005253043,0.0001767983,0.001182199,0.00006016131,0.00003427151,0.00004668671,0.003227797,0.03317424,0.001282608,0.0001915372,0.000247965,0.9603232],"study_design_scores_gemma":[0.01079257,0.002285471,0.003275789,0.0008883707,0.00004523265,0.00002019771,0.0008086154,0.1702182,0.6019233,0.02337422,0.1846413,0.001726766],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8747566,0.004669531,0.1157167,0.000534051,0.0003600848,0.001664132,0.00001783629,0.000495101,0.001785935],"genre_scores_gemma":[0.9984906,0.0002008378,0.0003799643,0.0001830606,0.0004054252,0.0002273234,0.00001029286,0.000025414,0.00007711833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9585965,"threshold_uncertainty_score":0.5532593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02238343315062185,"score_gpt":0.2962310319738665,"score_spread":0.2738475988232446,"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."}}